Methods and systems for vehicle control

Abstract

A computer-implemented method and system of controlling operation of a vehicle within a geographical area by a control system. The vehicle comprising a control module and an internal combustion engine

Description

FIELD The present disclosure general relates to methods and systems for controlling vehicles e.g. in smart cities. BACKGROUND Presently, the majority of road vehicles, including private transportations (e.g. cars), public transportations (e.g. buses) and goods transportations (e.g. lorries), are driven by an internal combustion engine fueled e.g. by diesel or gasoline. In addition to releasing energy, combustion of the fuel generates exhaust gas which contains a variety of pollutants harmful to the environment and to human health. The pollutants generally include particulate matter (PM) that can cause respiratory problems in humans when inhaled, and gasses such as oxides of carbon (COx) including carbon dioxide (CO2) and carbon monoxide (CO), oxides of nitrogen (NOx), and hydrocarbons (HC), amongst others. Exhaust gas from a vehicle engine may be treated before being released to the atmosphere to reduce the amount and / or impact of the pollutants. For example, exhaust gas may pass through a catalytic converter to reduce the amount of CO and NOx and a particulate filter to reduce the amount of PM, and Exhaust Gas Regeneration (EGR) may be employed to recirculate a portion of the exhaust gas back into the combustion chamber of the engine to reduce the amount of NOx emission. In order to remove a buildup of particulates in a diesel vehicle's particulate filter, regeneration may be performed periodically by injecting extra fuel into the engine to raise the temperature of the resulting exhaust gas so as to burn off the particulate matter when it passes through the filter. However, as well as more polluting gasses being produced as a result of burning extra fuel, during regeneration, black smoke containing finer particulate matter is often released in the exhaust that is particularly harmful to human health. In densely populated areas such as towns and cities, uncontrolled emission of pollutants by vehicles can result in serious health issues for inhabitants and visitors. At present, local authorities of individual towns, cities and regions may introduce regulations to restrict the amount of emission within its boundaries such as by imposing a charge or a fine, with measures such as "Ultra Low Emission Zones" (ULEZs), "Clean Air Zones" (CAZs), or "Low Emissions Zones" (LEZs). This methodology of managing emissions in specific locations can be effective; however, in order to implement such a strategy successfully installation of new or additional monitoring infrastructure is often required. Moreover, restriction through regulations requires compliance and may not achieve immediate effect. Further, there may be socioeconomic concerns over such measures - economically disadvantaged individuals for whom newer vehicles with higher Euro Emission Standards are unaffordable may be penalised for driving older vehicles in the restricted areas, and therefore unfairly affected by such measures. It is therefore desirable to provide a method and system for actively managing individual vehicles as well as traffic within a geographical area as a whole. SUMMARY An aspect of the technology disclosed herein provides a computer-implemented method of controlling operation of a vehicle within a geographical area by a control system, the control system being configured to communicate with a control module on the vehicle and with one or more data collection devices deployed within the geographical area, the control module being capable of controlling operation of an engine of the vehicle, the method comprising: receiving area-specific data from the one or more data collection devices; determining at least one control objective based on the received area-specific data; determining a vehicle control parameter based on the at least one control objective; and communicating the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter. Another aspect of the present technology provides a system for controlling operation of a vehicle within a geographical area, comprising: at least one processor; communication interface configured to communicate with a control module on the vehicle and with one or more data collection devices deployed within the geographical area, the control module being capable of controlling operation of an engine of the vehicle; and a non-transitory computer-readable medium comprising machine-readable software codes, which, when executed by the at least one processor, causes the at least one processor to: receive, via the communication interface, area-specific data from the one or more data collection devices; determine at least one control objective based on the received area-specific data; determine a vehicle control parameter based on the at least one control objective; and communicate, via the communication interface, the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter. Embodiments disclosed herein provide methods and systems for controlling operation of a vehicle within a geographical area such as within the boundary of a town or city, or an area or street within the town or city. Such a control system may be centrally implemented by the town or city authority to control or otherwise influence the operation of vehicles within the boundary of the geographical area. According to the embodiments, the control system communicates with a variety of data collection devices (e.g. traffic cameras, air quality sensors, etc.) and receives area-specific data (e.g. traffic count, pollutant levels, etc.) from the data collection devices e.g. at regular intervals. The control system then determines a control objective for the geographical area on the basis of the area-specific data received from the data collection devices. For example, the area-specific data may indicate that NOx level is too high in the geographical area, then the control system may determine the control objective is to reduce NOx level in the geographical area. Based on the determined control objective, the control system determines a vehicle control parameter that is communicated to a control module on the vehicle to coordinate its operation. For example, the control system may determine that the vehicle control parameter is an increased amount of exhaust gas being recirculated back to the vehicle engine. According to the embodiments, the vehicle is provided with a control module capable of controlling the operation of the vehicle engine. Upon receiving the vehicle control parameter from the control system, the control module may straightforwardly implement the vehicle control parameter or adapt it according to the specification of the vehicle, to bring the operation of the vehicle engine in line with the vehicle control parameter, e.g. by adjusting the amount of exhaust gas to be recirculated. It will be appreciated that the control system may be easily upscaled to control the operation of a large number of vehicles within the geographical area, such as fleets of public transport, to centrally control various aspects of operation of these vehicles such as the emission of various air pollutants. In doing so, it is possible for the control system to directly and dynamically influence various aspects of the environment of the geographical area, such as air quality, traffic density, noise level, etc. In some embodiments, determining at least one control objective based on the received area-specific data and / or determining a vehicle control parameter based on the at least one control objective may be performed by a machine learning algorithm, MLA, executing on the control system, the MLA having been previously trained to determine a control objective based on the area-specific data and / or determine a vehicle control parameter based on a control objective. The MI_A employed by the embodiments disclosed herein may be any suitable MLA as desired. In some embodiments, the MLA may be a recurrent neural network, RNN. In some embodiments, the at least one control objective may comprise a plurality of control objectives, and determining a vehicle control parameter based on the at least one control objective may comprise selecting one control objective out of the plurality of control objectives according to a priority of one or more control objectives of the plurality of control objectives. For example, the area-specific data may simultaneously indicate that the NOx level and the particulate level are both high in the geographical area, and the control system may determine a control objective of reducing NOx level and a control objective of reducing particulate level. From the two control objectives, the control system may select one control objective to be used to determine a vehicle control parameter depending on any priority attached to one or each of the two control objectives. For example, particulate level may be given a higher priority for the specific geographical area e.g. because the geographical area is a densely populated area and high particulate level is harmful to health. The priority of various control objectives may be predetermined and stored e.g. in a database accessible to the control system, such that whenever the control system determines that there are two or more control objectives based on the received area-specific data, the control system may refer to the predetermined priority to select the control objective with the highest priority. In some embodiments, the MLA may be trained to determine a respective priority for one or more control objective out of a plurality of control objectives, wherein, when the at least one control objective comprises a plurality of control objectives, determining a vehicle control parameter based on the at least one control objective may comprise the MLA determining a priority of one or more control objectives of the plurality of control objectives and selecting one control objective out of the plurality of control objectives according to the determined priority. In doing so, it is possible to account for different factors, e.g. time of day, day of week, traffic condition, etc., that may affect the priority of various control objectives, and enable the control system to dynamically determine a control objective with the highest priority according to these factors. In some embodiments, the at least one control objective may comprise reducing air pollutant level including carbon dioxide level, nitrogen oxide level, particulate level, and / or hydrocarbon level, increasing vehicle fuel efficiency, reducing traffic density within the geographical area, reducing noise level within the geographical area, and any combination thereof. Some control objectives may relate to the general air quality of the geographical area, some may relate to efficient traffic control, while some control objectives may relate to cost reduction (e.g. improving fuel economy). In some embodiments, determining a vehicle control parameter may comprise determining a setting for an engine control parameter of the vehicle, the engine control parameter being a parameter that controls operation of the vehicle's engine. In some embodiments, the engine control parameter may comprise a timing of diesel particulate filter regeneration cycle, an amount of exhaust gas to be recycled, a frequency or occurrence of exhaust gas regeneration cycle, a fuel injection rate, a fuel injection amount, or a combination thereof. Recirculating some or all of the exhaust gas from the vehicle's engine back into the combustion chamber, a process known as exhaust gas regeneration EGR, is known to reduce the resulting amount of NOx being released. However, exhaust gas regeneration reduces the operating temperature of the engine which results in an increased amount of particulate matter being released in the exhaust gas. Thus, depending on the control objective to be achieved, the use of EGR may or may not be desirable. In some embodiments, communicating the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter may comprise instructing the vehicle's control module to: operate exhaust gas regeneration to reduce a nitrogen oxide emission; or halt exhaust gas regeneration to control particulate emission. It is known that higher engine operating temperature reduces the amount of particulate matter being released in the exhaust gas, while lower engine operating temperature reduces the amount of NOx. Thus, in some embodiments, communicating the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter may comprise instructing the vehicle's control module to: increase fuel injection rate and / or fuel injection amount to reduce particulate emission; or reduce fuel injection rate and / or fuel injection amount to reduce a nitrogen oxide emission. During a diesel particulate filter (DPF) regeneration cycle, additional fuel is injected into the combustion chamber to increase the combustion temperature in the engine. In doing so, particulate matter trapped in the DPF is burnt off. However, this process releases black smoke that contains smaller-sized particulate matter that is more harmful to health. Thus, it is desirable to control the vehicle such that DPF regeneration cycle is performed within a designated zone that is deemed safe, for example in an unpopulated area. In some embodiments, the area-specific data may comprise an indication of whether the geographical area is a designated zone, and the operation instruction may comprise: halting a scheduled diesel particulate filter regeneration cycle to reduce particulate emission in the geographical area when the geographical area is not a designated zone; or advancing a scheduled diesel particulate filter regeneration cycle to clean the diesel particulate filter when the geographical area is a designated zone. In some embodiments, the area-specific data may comprise: air quality data; emission data including one or more of carbon dioxide emission level, nitrogen oxide emission level, particulate emission level; traffic flow data including one or more of vehicle count traffic density, traffic speed, traffic direction; noise level; population density; or a combination thereof. In some embodiments, determining at least one control objective based on the received area-specific data may comprise comparing the area-specific data with a corresponding reference value and setting the at least one control objective based on results of the comparison. For example, the control system may analyse the received area-specific data based on various predetermined reference values on various data (e.g. limits on various pollutant levels). These reference values may be set specifically for the geographical area depending on the characteristics of the geographical area such as the type of area (e.g. town centre, residential area, area with vulnerable population such as children, etc.), or they may be set according to one or more laws and regulations. In some embodiments, comparing the area-specific data with a corresponding reference value may comprise one or more of: comparing a current carbon dioxide emission level of the geographical area with a carbon dioxide emission limit; comparing a current nitrogen oxide emission level of the geographical area with a nitrogen oxide emission limit; comparing a current particulate emission level of the geographical area with a particulate emission limit; comparing a current vehicle count or a current traffic density of the geographical area with a predetermined maximum vehicle number for the geographical area; or comparing a current noise level with a predetermined maximum noise level. There may be situations where the area-specific data is within an acceptable range but there may be social concerns that can be usefully addressed. Thus, in some embodiments, the at least one control objective may further comprise a predetermined social objective. In some embodiments, the predetermined social objective may comprise, with respect to a designated area, optimising fuel efficiency for the vehicle, minimising carbon dioxide emission, minimising nitrogen oxide emission, minimising particulate emission, minimising noise level, diverting traffic flow, or a combination thereof. For example, when all area-specific data is within an acceptable range, it may be useful to reduce fuel consumption both for environmental concern and cost consideration, in which case the control system may determine a social objective to optimise fuel efficiency for the vehicle e.g. by instructing the control module of the vehicle to adjust the operation of the engine accordingly. The control system may be arranged to communicate with the traffic infrastructure within the geographical area to enable active traffic control. Thus, in some embodiments, the method may further comprise sending an instruction to one or more traffic infrastructure to modify operation thereof based on the at least one control objective, the instruction may comprise one or more of: altering a duration of one or more traffic lights to control a flow of traffic; lifting a barrier to one or more restricted public transport lanes to divert traffic onto the one or more restricted public transport lanes; removing one or more emission control zones to redistribute traffic; changing one or more road signs to indicate lifting of restrictions against use of a public transport lane; changing one or more road signs to vary a speed limit on one or more road sections; changing one or more road signs to instruct drivers of hybrid vehicles to switch to electric mode; and / or changing one or more road signs to instruct drivers to switch to an ECO mode. In some embodiments, the data collection devices may comprise one or more traffic cameras, one or more close circuit televisions, one or more sound recognition devices, one or more car counters, one or more air quality sensors, and / or one or more tolling gates. In some embodiments, the vehicle may comprise a hydrogen injection unit configured to generate hydrogen through electrolysis and introduce the generated hydrogen to the internal combustion engine, and the method may further comprise determining the vehicle control parameter to be operation of the hydrogen injection unit. A further aspect of the present technology provides a computer-readable storage medium comprising machine-readable code which, when executed by a processor, causes the processor to perform the method as described above. A yet further aspect of the present technology provides a hydrogen injection unit provided to a vehicle comprising a control module and an internal combustion engine, the hydrogen injection unit being configured to generate hydrogen through electrolysis and introduce the generated hydrogen into the internal combustion engine of the vehicle, wherein operation of the hydrogen injection unit is controlled by the control module in communication with the system as described above under instruction of the system. In the context of the present disclosure, the expression "computer-readable storage medium" is intended to include media of any nature and kind whatsoever, including RAM, ROM, disks (CD-ROMs, DVDs, floppy disks, hard drivers, etc.), USB keys, solid state-drives, tape drives, etc. Implementations of the present technology each have at least one of the above-mentioned objects and / or aspects, but do not necessarily have all of them. It should be understood that some aspects of the present technology that have resulted from attempting to attain the above-mentioned object may not satisfy this object and / or may satisfy other objects not specifically recited herein. Additional and / or alternative features, aspects and advantages of implementations of the present technology will become apparent from the following description, the accompanying drawings and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS Embodiments will now be described, with reference to the accompanying drawings, in which: FIG. 1 shows a schematic diagram of an exemplary computer system; FIG. 2 shows a network computing environment for a vehicle; FIG. 3 shows a functional diagram of an exemplary control module onboard a vehicle; FIG. 4 shows schematically a network environment for an exemplary control system; and FIG. 5 shows a flow diagram of an exemplary method of controlling operation of a vehicle. DETAILED DESCRIPTION Embodiments disclosed herein provide computer-implemented methods and systems for centrally controlling the operation of a vehicle. The vehicle generally comprises an internal combustion engine controlled by an engine control unit, and is provided with a control module arranged to communicate with the engine control unit. The control system according to the embodiments communicate with the control module of the vehicle, and the engine control unit receives instructions from the control module to bring the operation of the vehicle engine in line with one or more control objectives implemented by the control system. An overview of a vehicle control module suitable for use with the present control methods and systems is given below. Computer System Referring first to FIG. 1, there is shown a computer system 100 suitable for use with some implementations of the present technology, for example to implement a control module. The computer system 100 comprises various hardware components including one or more single or multi-core processors collectively represented by processor 110, a solid-state drive 120, a user interface 130 for receiving user input, a memory 140, which may be a randomaccess memory or any other type of memory, and a network interface 150 for communicating with one or more other devices via a communication network. Communication between the various components of the computer system 100 may be enabled by one or more internal and / or external buses (not shown) (e.g. a PCI bus, universal serial bus, IEEE 1394 "Firewire" bus, SCSI bus, Serial-ATA bus, etc.), to which the various hardware components are electronically coupled. According to embodiments of the present technology, the solid-state drive 120 stores program instructions suitable for being loaded into the memory 130 and executed by the processor 110 for controlling the performance of a vehicle. For example, the program instructions may be part of a vehicle control application executable by the processor 110. It is noted that the computer system 100 may have additional and / or optional components (not depicted), such as network communication modules, localization modules, and the like. In some embodiments, a user electronic device, such as a smartphone, a tablet computer or a separate control device on the vehicle, may be arranged to connect to the control module to enable network capability of the control module and / or to allow greater user control. The user electronic device may be coupled to a server via a communication network. In the context of the present disclosure, a "server" may be a computer program that is running on appropriate hardware and capable of receiving requests (e.g. from devices) over a network, and carrying out those requests, or causing those requests to be carried out. The hardware may be a physical computer or a physical computer system, but neither is required to be the case with respect to the present technology. In the present context, the use of the expression a "server" is not intended to mean that every task (e.g., received instructions or requests) or any particular task will have been received, carried out, or caused to be carried out, by the same server (i.e., the same software and / or hardware); it is intended to mean that any number of software elements or hardware devices may be involved in receiving / sending, carrying out or causing to be carried out any task or request, or the consequences of any task or request; and all of this software and hardware may be one server or multiple servers, both of which are included within the expression "at least one server". In the context of the present disclosure, a "database" is any structured collection of data, irrespective of its particular structure, the database management software, or the computer hardware on which the data is stored, implemented or otherwise rendered available for use. A database may reside on the same hardware as the process that stores or makes use of the information stored in the database or it may reside on separate hardware, such as a dedicated server or plurality of servers. In the context of the present disclosure, the expression "information" includes information of any nature or kind whatsoever capable of being stored in a database. Thus, information includes, but is not limited to audiovisual information (images, sound records, etc.), data (location data, numerical data, route data, etc.), text (driver identification, route identification, etc.), documents, spreadsheets, lists of words, etc. In the context of the present disclosure, the expression "component" or "module" is meant to include any software (appropriate to a particular hardware context), hardware, or a combination of both, that is both necessary and sufficient to achieve the specific function(s) being referenced. Network Computer Environment FIG. 2 illustrates a network computer environment 200 suitable for use with some embodiments of the vehicle control module of the present technology. The networked computer environment 200 comprises an electronic device 210 (e.g. a smartphone or onboard computer) associated with a vehicle 220 or the user / driver of the vehicle 220, a server 230 in communication with the electronic device 210 via a communication network 240 (e.g. the Internet or the like). Optionally, the network computer environment 200 can also include a GPS satellite (not depicted) transmitting and / or receiving a GPS signal to / from the electronic device 210. It will be understood that the present technology is not limited to GPS and may employ a positioning technology other than GPS. It should be noted that the GPS satellite can be omitted altogether. The vehicle 220 to which the electronic device 210 is associated may comprise any leisure or transportation vehicle such as a private or commercial car, truck, motorbike, buses or the like. The vehicle may be user operated or a driverless vehicle. It should be noted that specific parameters of the vehicle 220 are not limiting, these specific parameters including: vehicle manufacturer, vehicle model, vehicle year of manufacture, vehicle weight, vehicle dimensions, vehicle weight distribution, vehicle surface area, vehicle height, drive train type (e.g. 2x or 4x), tyre type, brake system, fuel system, mileage, vehicle identification number, and engine size. The implementation of the electronic device 210 is not particularly limited, but as an example, the electronic device 210 may be implemented as a vehicle engine control unit, a vehicle CPU, a vehicle navigation device (e.g. TomTomTM, GarminTM), a tablet, a personal computer built into the vehicle 220 and the like. Thus, it should be noted that the electronic device 210 may or may not be permanently associated with the vehicle 220. Additionally or alternatively, the electronic device 210 can be implemented in a wireless communication device such as a mobile telephone (e.g. a smartphone or a radio-phone). In certain embodiments, the electronic device 210 has a user interface such as a display 270. The electronic device 210 may comprise some or all of the components of the computer system 100 depicted in FIG. 1. In some embodiments, the electronic device 210 is an onboard computer device (implementing a control module) and comprises the processor 110, solid-state drive 120 and the memory 130. In other words, the electronic device 210 comprises hardware and / or software and / or firmware, or a combination thereof, for controlling the performance of the vehicle 220, as will be described in greater detail below. In some examples, the electronic device 210 may further comprise or have access to a plurality of sensors. The plurality of sensors may include a camera for capturing an image of a surrounding area, a GPS signal receiver, as well as various performance-related sensors such as emission sensors. The sensors are operatively coupled to the processor 110 of the computing system 100 for transmitting sensor data to the processor 110 for processing thereof. Communication network In some embodiments of the present technology, the communication network 240 is the Internet. In alternative non-limiting embodiments, the communication network can be implemented as any suitable local area network (LAN), wide area network (WAN), a private communication network or the like. It should be expressly understood that implementations for the communication network are for illustration purposes only. Connection to the communication network 240 enables (a control module implementing on) the electronic device to communicate with a centralized control system, described in detail below. A communication link (not separately numbered) between the electronic device 210 and the communication network 240 is implemented depending on how the electronic device 210 is implemented. For example, where the electronic device 210 is implemented as a wireless communication device such as a smartphone or a navigation device, the communication link can be implemented as a wireless communication link. Examples of wireless communication links include, but are not limited to, a 3G, 4G or 5G communication network link, and the like. The communication network 240 may also use a wireless connection with the server 230. Server In some embodiments of the present technology, the server 230 is implemented as a conventional computer server and may comprise some or all of the components of the computer system 100 of FIG. 1. The server 230 may be implemented in any suitable hardware, software, and / or firmware, or a combination thereof. In the depicted embodiment in FIG. 2, the server 230 is a single server. However, in alternative embodiments (not shown), the functionality of the server 230 may be distributed and may be implemented via multiple servers. In some embodiments of the present technology, the processor 110 of the electronic device 210 can be in communication with the server 230 to receive one or more updates. The updates can be, but are not limited to, software updates, map updates, routes updates, weather updates, and the like. In some embodiments of the present technology, the processor 110 can also be configured to transmit to the server 230 certain operational data, such as routes travelled, traffic data, performance data, and the like. Some or all data transmitted between the vehicle 220 and the server 230 may be encrypted and / or anonymized, for example to be included in a training database. The processor 110 of the server 230 has access to one or more machine learning algorithms, MLAs. In some embodiments, the control system of the present technology may be executing on the server 230; in other embodiments, the control system may be executing on computer system other than the server 230. In any case, the server 230 should be interpreted as a generic term referring to the functionality of a server of network of servers. The following gives a brief overview of a number of different types of machine learning algorithms for embodiment(s) in which one or more MLAs are used. However, it should be noted that the use of an MLA in these embodiment(s) is a non-limiting example of implementing the present technology, and the use of an MLA is not essential. Overview of MLAs There are many different types of MLAs known in the art. Broadly speaking, there are three types of MLAs: supervised learning-based MLAs, unsupervised learning-based MLAs, and reinforcement learning-based MLAs. Supervised learning MLA process is based on a target - outcome variable (or dependent variable), which is to be predicted from a given set of predictors (independent variables). Using this set of variables, the MLA generates a function using training data that maps inputs to desired outputs during training. The training process continues until the MLA achieves a desired level of accuracy on validation data. Examples of supervised learning-based MLAs include: Regression, Decision Tree, Random Forest, Logistic Regression, etc. Unsupervised learning MLA does not involve predicting a target or outcome variable but learns patterns from untagged data. Such MLAs are capable of self-organization to capture patterns as probability densities, and are used e.g. for clustering a population of values into different groups. Clustering is used in many fields including pattern recognition, image analysis, bioinformatics, data compression, computer graphics, etc. Examples of unsupervised learning MLAs include: apriori algorithm and k-means algorithm. Reinforcement learning MLA is trained to take actions or make decisions that maximize cumulative reward (e.g. a user-provided score). During training, the MLA is exposed to a training environment where it learns through trial and error to develop an optimal or near-optimal policy that maximizes reward. In doing so, the MLA learns from past experience and attempts to capture the best possible knowledge to make desirable decisions. An example of reinforcement learning MLA is a Markov Decision Process. It should be understood that different types of MLAs having different structures or topologies may be used for various tasks. One particular type of MLAs includes artificial neural networks (ANN), also known as neural networks (NN). Neural Networks (NN) Generally speaking, a given NN consists of an interconnected group of artificial "neurons", which process information using a connectionist approach to computation. NNs are used to model complex relationships between inputs and outputs (without actually knowing the relationships) or to find patterns in data. NNs are first conditioned in a training phase in which they are provided with a known set of "inputs" and information for adapting the NN to generate appropriate outputs (for a given situation that is being attempted to be modelled). During this training phase, the given NN adapts to the situation being learned and changes its structure such that the given NN will be able to provide reasonable predicted outputs for given inputs in a new situation (based on what was learned). Thus, rather than attempting to determine a complex statistical arrangements or mathematical algorithms for a given situation, the given NN aims to provide an "intuitive" answer based on a "feeling" for a situation. The given NN is thus regarded as a trained "black box", which can be used to determine a reasonable answer to a given set of inputs in a situation giving little importance to what happens inside the "box". NNs are commonly used in many such situations where an appropriate output based on a given input is important, but exactly how that output is derived is of lesser importance or is unimportant. For example, NNs are commonly used to optimize the distribution of web-traffic between servers and in data processing, including filtering, clustering, signal separation, compression, vector generation and the like. Deep Neural Networks In some non-limiting embodiments of the present technology, the NN can be implemented as a deep neural network. It should be understood that NNs can be classified into various classes of NNs. Below are a few non-limiting example classes of NNs. Recurrent Neural Networks (RNNs) RNNs are adapted to use their "internal states" (stored memory) to process sequences of inputs. This makes RNNs well-suited for tasks such as unsegmented handwriting recognition and speech recognition, for example. These internal states of the RNNs can be controlled and are referred to as "gated" states or "gated" memories. It should also be noted that RNNs themselves can also be classified into various sub-classes of RNNs. For example, RNNs comprise Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional RNNs (BRNNs), and the like. LSTM networks are deep learning systems that can learn tasks that require, in a sense, "memories" of events that happened during very short and discrete time steps earlier. Topologies of LSTM networks can vary based on specific tasks that they "learn" to perform. For example, LSTM networks may learn to perform tasks where relatively long delays occur between events or where events occur together at low and at high frequencies. RNNs having particular gated mechanisms are referred to as GRUs. Unlike LSTM networks, GRUs lack "output gates" and, therefore, have fewer parameters than LSTM networks. BRNNs may have "hidden layers" of neurons that are connected in opposite directions which may allow using information from past as well as future states. Residual Neural Network (ResNet) Another example of the NN that can be used to implement non-limiting embodiments of the present technology is a residual neural network (ResNet). Deep networks naturally integrate low / mid / high-level features and classifiers in an end-to-end multilayer fashion, and the "levels" of features can be enriched by the number of stacked layers (depth). Convolutional Neural Network (CNN) CNNs are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. They are most commonly applied to analyze visual imagery and have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain-computer interfaces, and financial time series. CNNs are regularized fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. CNNs use relatively little preprocessing compared to other image classification algorithms and learn to optimize the filters (or kernels) through automated learning. To summarize, the implementation of at least a portion of the one or more MLAs in the context of the present technology can be broadly categorized into two phases - a training phase and an in-use or deployed phase. First, the given MLA is trained in the training phase using one or more appropriate training data sets. Then, once the given MI_A learned what data to expect as inputs and what data to provide as outputs, the given MLA is executed using in-use data in the in-use or deployed phase. Further, while deployed, the given MUX may continue to learn from the in-use data based for example on user feedback. Vehicle control system FIG. 3 shows a functional diagram of an exemplary control module as part of a vehicle control system for controlling the operation of a vehicle. In the present example, the system comprises a control module 310, which for example may be implemented as a software module in an electronic device such as a smartphone, or as a onboard control device provided on the vehicle. The control module 310 comprises a processor 311 for executing software and / or firmware instructions to carry out one or more functions, including e.g. executing one or more MLAs. It should be noted that, while only one processor 311 is depicted, more than one processor may be provided to the control module 310 in other embodiments. The control module 310 is in communication (e.g. via a wireless connection) with an external server 320 via an input / output interface 312, for software and / or firmware updates, accessing, downloading and / or updating one or more MLAs, sending and retrieving data, amongst other things. In addition, the control module is in communication (e.g. via a wireless or wired connection) with an engine control unit 330 of the vehicle 220 via respective input / output interface 312 and 332. It should be noted that the implementation of one or more MLAs is optional. The engine control unit 330 comprises a processor 331 for executing instructions, amongst other things, to control the performance of engine 340 of the vehicle 220 via the input / output interface 332. It should be noted that, while only one processor 331 is depicted, more than one processor may be provided to the engine control unit 330 in other embodiments. The engine control unit 330 controls the performance of the engine 340 by operating the engine according to a parameter set for each operation element of the engine. In the present embodiment, the engine 340 is operated through controlling a plurality of operation elements, which for example may include fuel injection into the engine, regeneration cycle, etc. (described in more detail below). Exhaust gas, including e.g. oxides of nitrogen (NOx), oxides of carbon (COx), particulate matter (PM) and unburnt fuel, from the engine 340 is passed through an exhaust system 350 for processing before being released into the atmosphere. In the present embodiment, the exhaust system 350 may comprise one or more cataclysmic converters for reducing the amount of NOx and carbon monoxide (CO), and one or more particulate filters for filtering out at least some of the PM in the exhaust gas. In the present example, the vehicle 220 is provided with a plurality of sensors 360 for collecting data on one or more aspects of the performance of the vehicle. For example, the sensors 360 may include a temperature sensor for determining an operating temperature of the engine, a NOx sensor for determining the amount of NOx in the processed exhaust gas, a CO sensor for determining the amount of CO in the processed exhaust gas, a PM sensor for determining the amount of PM in the processed exhaust gas, a sensor for determining the PM buildup in the PM filter, etc. The sensors 360 may also include an odometer, a speedometer, a fuel gauge, etc. The sensor data is feedback to the engine control unit 330 via the input / output interface 332 to be used, for example, to adjust the parameters for the operation elements of the engine. Sensor data received by the engine control unit 330 can be sent to the control module 310 e.g. as inputs to an MLA which may be trained to determine appropriate operation parameters based on received sensor data. In present embodiments, the processor 311 of the control module 310 receives control information via the I / O interface 312, e.g. from a centralized control system, to control the operation of the vehicle engine 340 and exhaust system 350 via the engine control unite 330. In some embodiments, the control module may be configured to select an appropriate operation mode amongst a plurality of operation modes for controlling the engine 340 based on the received control information. An operation mode is a set of operation parameters for the engine 340 and / or the exhaust system 350 with a specific objective, for example a school mode that may be selected when near a school (or similar establishment) with an objective of reduced harmful emissions. Each of the plurality of operation modes is associated with a specific objective (or multiple objectives) and determines the parameters for one or more operation elements of the engine 340 and exhaust system 350, such as an amount of fuel injected into the engine 340. In the present embodiment, control objectives may include a limit on PM emission, a limit on NOx emission, a limit on CO emission, a fuel efficiency target, etc. In the present example, upon receiving control information from the centralized control system, the processor 311 determines one or more operation parameters to coordinate the vehicle with the received control information. In some embodiments, the control information may include one or more operation parameters for the vehicle and the processor may straightforwardly implement the one or more operation parameters included in the control information. In other embodiments, the control information may include only one or more objectives and the processor may determine the appropriate operation parameters to bring the vehicle's operation in line with the one or more objectives. The one or more operation parameters or one or more objectives received from the control information may match a preset operation mode on the control module 310, in which case the processor 311 selects an operation mode that corresponds to the one or more operation parameters or one or more objectives, and sends the parameters for the operation elements of the engine determined by the selected operation mode to the engine control unit 330. Otherwise, the processor 311 simply sends the received or determined operation parameters to the engine control unit 330. Then, upon receiving the parameters, the processor 331 of the engine control unit 330 executes instructions using the parameters to operate the engine 340. Centralized control system The Applicant recognized that diesel engine vehicles are widely used in the transportation industry, and the Engine Control Unit (ECU), a unit that is onboard a diesel vehicle configured to control the operation of the vehicle's engine, may be manipulated to control the performance of the diesel vehicle e.g. to control emissions from the vehicle. The ECU is a device that controls various systems within a diesel engine, including (but not limited to) fuel injection, ignition timing, Exhaust Gas Recirculation (EGR), Diesel Particle Filtration (DPF) regeneration, and emissions control. The present technology thus provides a control system that can be implemented on a geographical scale, instead of on an individual vehicle scale, capable of enforcing one or more area-wide objectives that override individual vehicle objectives. The control system may, for example, be implemented in the form of a digital twin transport system of a smart city. Decisions on what objective or objectives to enforce, the priority of various objectives, and how the objective or objectives translate into a vehicle's operation parameters may be performed by a previously trained MLA. Depending on the needs, regulations and characteristics of individual geographical area, (the MLA executing on) the control system may be configured to consider any number and any form of objectives, including environmental objectives, safety objectives, social objectives, economic objectives, traffic control objectives, etc. Some nonlimiting examples include: Traffic management The control system may be arranged to communicate with smart infrastructure (data collection devices), such as (but not limited to) automatic traffic cameras (ATC), closed-circuit TVs (CCTV), sound recognition devices, vehicle counters, tolling gates, already deployed in a city. This smart infrastructure relays data that is specific to an area, e.g. the composition and flow of traffic, to the control system. The control system may then autonomously determine whether changes are needed to control traffic flow. This may include, for example, diverting a portion of the traffic to use different lanes or take different routes, adjusting the timing of one or more traffic lights, etc. Such changes may for example be implemented through other smart infrastructure such as the lifting of barriers that normally prevent the use of certain lanes (e.g. public transport lanes), changing electronic road signs to indicate use of additional lanes, changing electronic road signs to adjust a local speed limit, adjusting the timing or sequencing of dynamic traffic light systems, and / or providing recommendations for alternative routes via a communication device (e.g. a control module) onboard a vehicle to reduce traffic density in a specific area. Active air quality management The control system is arranged to communicate with a control module onboard a vehicle, the control module being configured to control the operation of the vehicle e.g. via the vehicle's engine control unit. In doing so, local authorities (e.g. town / city governance, law enforcement, etc.) is able to directly control the amount and / or type of emissions predominantly produced by vehicles within their geographical area through influencing the operation of vehicles' engines. The control system therefore enables local authorities to mitigate emissions when necessary, for example when local air quality sensors indicate that pollutant levels are dangerously high. Such air-quality objectives may include (but not limited to) a reduction on CO2 emission, a reduction of NOx emission, a reduction of particulate matter emission, a reduction of hydrocarbon emission, etc., and one or more objectives may be translated into one or more vehicles operation parameters (e.g. engine operation parameters) and communicated to (the control modules of) the vehicles in the geographical area. The operation of the vehicles, once received the operation parameters, may be brought in line with the one or more objectives e.g. by an onboard control module, to reduce one or more types of emission. Fuel efficiency The control system may be configured to implement socio-economic objectives such as supporting fuel efficiency for individual vehicles. Through the control system, local authorities may facilitate vehicle owners to reduce fuel costs by implementing objectives that optimise or otherwise improve the fuel efficiency of vehicles. Such objectives may be translated into engine operation parameters and communicated to individual vehicles in the geographical area such that operation of the vehicles is brought in line with the objectives. For example, such objectives may be implemented during periods of low-density traffic flow, e.g., Sunday evening, and may be supported by smart infrastructure to e.g. indicate an upward adjustment of the local speed limit that is optimal for fuel efficiency. Vulnerable populations The control system may be configured to implement social objectives aimed at protecting the vulnerable population within the geographical area. For example, designated areas may be identified as being populated with vulnerable groups such as the elderly, children, and those with pre-existing medical conditions (e.g. hospitals, hospices). The control system can use the received area-specific data to determine whether data indicating poor air quality overlaps with a designated area, such as hospitals, nurseries, doctors' surgeries, schools, care homes, hospices, and day care centres, and select one or more appropriate objectives to influence the operation parameters of a vehicle when entering the designated area, for example, even if the vehicle is operating within acceptable ranges to reduce one or more types of emission or to reduce speed, etc. A control module capable of controlling the operation of a vehicle, e.g. through, amongst other things, controlling the operation the vehicle's engine control unit (ECU), may be retrofitted to the vehicle to allow the vehicle to connect to the centralised control system to enable direct and large-scale implementation of environmental, social, economic and legislative objectives within a specified geographical area. Specific objectives may be selected and implemented in large scale across the whole geographical area to improve the efficiency, safety, and sustainability of public and commercial transportation systems. Moreover, vehicles equipped with advanced technologies such as sensors, network connectivity, and autonomous capabilities may be requested to comply with objectives set by the control system to optimize traffic flow, reduce emissions, increase energy efficiency, and to enhance data collection and analysis to improve e.g. the relevance and effectiveness of future objectives. As discussed above, the control module onboard a vehicle may manipulate the vehicle's ECU to change the composition of emissions through modifying one or more of the following parameters: • The vehicle's diesel particulate filter (DPF) regeneration cycles. During a vehicle performs a DPF regeneration cycle, additional (more than the normal amount of) fuel is injected into the combustion mixture in the engine combustion chamber to increase the combustion temperatures (or engine operating temperature). The increase in combustion temperature results in an increase in temperature in the exhaust system, which incinerates the particulates trapped in the DPF. However, a by-product of DPF regeneration is smaller particulates which are released to the atmosphere (which can be seen as black smoke) to potentially harmful levels. Enabling the vehicle's DPF regeneration cycle to be delayed to a later time or advanced to an earlier time such that the regeneration cycle occurs while the vehicle is in a designated zone or area means that necessary but harmful emission may be kept within a zone or area that is deemed safe for its release. Such a designated zone or area may, for example, be unpopulated areas such as motorways or industrial areas. • The vehicle's exhaust gas recirculation (EGR) function. EGR determines the amount of exhaust gas that is recirculated back into the combustion chamber of the vehicle's engine. Replacing a portion of atmospheric air with a portion of exhaust gas reduces the amount of oxygen in the combustion chamber, and the lower oxygen content leads to a lower engine operating temperature which results in less NOx being produced. However, lower engine operating temperature also leads to more particulate matter being produced. Thus, by modifying the occurrence of EGR. and / or the amount of exhaust gas to recirculate, it is possible to influence the amount of both PM and NOx produced in the combustion process in an inverse relationship - higher engine temperatures, lower PM but higher NOx; lower engine temperatures, higher PM but lower NOx. • Fuel injection rate or amount. Altering how quickly and / or how much fuel is injected into the combustion chamber again leads to a change in the engine operating temperature. More fuel, higher temperature. Thus, modifying the amount of fuel and / or the rate at which fuel is added to the combustion mixture influences the production of NOx and PM. Thus, by communicating in large scale with control modules of vehicles, it is possible for a control system according to the present technology to directly and dynamically influence the amount of pollutant emissions released by vehicles. Embodiments of the control system can be arranged to communicate with existing smart (network-enabled) sensors and infrastructure already installed or deployed within the geographical area controlled by the control system. The control system thus comprise communication circuitry or communication module configured to enable communication between the control system of the sensors / infrastructure and preferably one or more databases to enable the received data to be stored for immediate processing or later analyses. In some embodiments, artificial intelligence e.g. one or more machine learning algorithms (MLA) may be employed to process the data, for example to determine one or more appropriate control objectives for the geographical area and suitable vehicle control parameters to be communicated to vehicles within the geographical area to implement the control objective(s). These geographically situated objectives will be dynamically and constantly changed according to variations in the data collected from the sensors and infrastructure. The vehicle control parameter(s) is communicated to the control modules of the vehicles and relay to respective engine control units of the vehicles to control the vehicles' engine operation where appropriate. Furthermore, the control system is capable of communicating with the smart Infrastructure to influence traffic flow, for example: • Dynamic traffic lights. Altering the duration and / or sequence of traffic lights based on data from ATCs, AN PR, traffic sensor cables, and a plurality of other traffic density sensing devices to improve traffic flow. • Smart bus gates. Lifting bus gates during times of peak traffic flow to remove restrictions on public transport lane(s) to allow private vehicles the use of an additional lane to navigate across the geographical area. • Removal of a ULEZ. Temporary removal of ULEZ to encourage vehicles to spread across a wider area instead of funnelling vehicles down certain routes (that are outside of the ULEZ). • Smart road signs. Electronic road signs may be dynamically programmed by the control system, for example: to indicate the lifting of restriction on the use of public transport lane during peak traffic flow; to vary speed limits to reduce the speed of vehicles in some areas (e.g. near schools during drop-off / pickup time) and / or to allow higher speed in other areas (e.g. commercial areas during night time); to request hybrid vehicles to switch to electric mode in designated zones (e.g. densely populated area, areas with schools, nurseries, care homes, etc.); to request applicable vehicles to switch to an ECO mode. Embodiments disclosed herein provide methods and systems for controlling operation of one or more vehicles within a geographical area, e.g. within the boundary of a town or city, or an area or a street within the town or city. Such a control system may be centrally implemented by the town or city authority or governance to control or otherwise influence the operation of vehicles within the boundary of its geographical area. An embodiment of a centralized control system is shown in FIG. 4. FIG. 4 shows an exemplary control system 400 comprising a communication module 410 which may include or has access to a data storage for storing data, and one or more data processor 420 which in some embodiments may have installed thereon a decision-making AI executing one or more MLAs. The control system 400 communicates, e.g. via the communication module 410, with a variety of data collection devices 430 (e.g. traffic cameras, air quality sensors, etc. and receives area-specific data (e.g. traffic count, pollutant levels, etc.) from the data collection devices 430 e.g. at regular intervals or upon request. The control system 400 then determines, e.g. by inputting the received data to the MLA executing on the processor(s) 420, a control objective for the geographical area on the basis of the area-specific data received from the data collection devices 430. For example, the area-specific data may indicate that NOx level is too high (e.g. higher than a predetermined threshold), and the processor(s) 420 may determine the control objective is to reduce NOx emission. The processor 420 may additionally take as input one or more manually input objectives 440 e.g. based on rules or regulations set by local governance or authority. Where more than one objective is determined and contradicting objectives are present, the MLA executing on the processor(s) 420 may be previously trained to determine a priority for the contradicting objectives to select one or more appropriate objectives to implement. Based on the determined control objective, (the processor 420 of) the control system determines a vehicle control parameter. In the present embodiment, a vehicle 460 is provided with a control module 462 capable of controlling the operation of the vehicle engine. Thus, the control system 400 communicates, via the communication module 410, to the control module 462 onboard the vehicle 460 to coordinate its operation. Using the example above, when the control system 400 determines that reducing the amount of NOx emission is the control objective, it may then determine that increasing the amount of exhaust gas being recirculated back to the vehicle engine is the vehicle control parameter. Upon receiving the vehicle control parameter from the control system 400, the control module 462 may straightforwardly implement the vehicle control parameter, or adapt it according to the specification of the vehicle 460 (e.g. to specify the amount or proportion of exhaust gas to be recirculated), and relay the control parameter to an engine control unit 464 that is configured to control the operation of the vehicle engine, so as to bring the operation of the vehicle engine in line with the received or adapted vehicle control parameter, e.g. by adjusting the amount of exhaust gas to be recirculated. In some embodiments, the vehicle 460 may be provided with a hydrogen injection unit 466, alongside the vehicle's internal combustion engine. The hydrogen injection unit 466 is configured to generate hydrogen in situ through a process of electrolysis of an electrolyte comprising a solvent (e.g. water) and one or more suitable types of salt. In operation, electrolysis of the electrolyte produces hydrogen and oxygen, which can be injected into the air manifold of the internal combustion engine. The introduction of hydrogen and additional oxygen into the engine promotes complete combustion of the fuel and as such improves the fuel efficiency of the vehicle as well as reducing the amount of CO2 and PM emissions. Thus, in embodiments where a hydrogen injection unit 466 is provided, activation or operation of the hydrogen injection unit 466 may be used as an option for controlling (reducing) the vehicle's CO2 and PM emissions if the control system 400 determines that a reduction of CO2 and / or PM emissions is the control objection for a geographical area. However, operation of the hydrogen injection unit 466 increases the amount of NOx emission due to higher combustion temperatures. Thus, (the processor 420 of) the control system 400 (and / or the control module 462 of vehicle) is preferably configured to determine if and when (and where geographically, e.g. when the geographical boundary within which the vehicle 460 has changed and the control system 400 is updated via the communication module 410) operating the hydrogen injection unit 466 is appropriate, for example using a suitably trained MLA. Through appropriately managing the timing of and the location at which hydrogen is introduced to a vehicle's engine by a hydrogen injection unit, it is possible to facilitate a reduction of CO2 and PM emissions within a specified geographical area without causing a detrimental impact on NOx concentrations in urban environments. It can be seen that the control system is easily scalable to control or coordinate the operation of a large number of vehicles, such as fleets of public transport, simultaneously within the geographical area to centrally control various aspects of operation of these vehicles. In doing so, it is possible for the control system to directly and dynamically influence various aspects of the environment of the geographical area, such as air quality, traffic density, noise level, etc. The control system 400 of the present embodiment is further arranged to communicate with existing traffic control infrastructure 450 in the geographical area. Thus, the control system 400 may implement one or more control objectives through centrally operating the traffic control infrastructure 450, such as modifying the sequence or duration of traffic lights, dynamically programming one or more electronic road signs, etc. FIG. 5 shows a flow diagram of an exemplary method of controlling operation of a vehicle within a geographical area by a control system, such as the control system 400. The control system is configured to communicate with a control module (e.g. control module 462) on the vehicle and with one or more data collection devices (e.g. data collection devices 430) deployed within the geographical area. The method begins at S510, at which the control system (e.g. via the communication module 410) receives area-specific data from the data collection devices. In some embodiments, the data collection devices may comprise one or more traffic cameras, one or more close circuit televisions, one or more sound recognition devices, one or more car counters, one or more air quality sensors, and / or one or more tolling gates. Then at S520, the control system (e.g. using processor 420) determines at least one control objective based on the received area-specific data, and at S530, determines a vehicle control parameter based on the at least one control objective. In some embodiments, the processor 420 of the control system 400 may have executing thereon an MLA, which has been previously trained to determine a control objective based on the area-specific data and / or determine a vehicle control parameter based on a control objective. The MLA used herein may be any suitable MLA as desired, such as a recurrent neural network, RNN. In some embodiments, the control system may determine at least one control objective based on the received area-specific data by comparing the area-specific data with a corresponding reference value, and setting the at least one control objective based on the results of the comparison. For example, the control system may analyse the received area-specific data based on various predetermined reference values that correspond to various data, such as various local, national or global limits on various pollutant levels. These reference values may be set specifically for the geographical area depending on the characteristics of the geographical area such as the type of area (e.g. town centre, residential area, area with vulnerable population such as children, etc.), or they may be set by laws and regulations. In some embodiments, the control system may compare the area-specific data with a corresponding reference value by: comparing a current carbon dioxide emission level of the geographical area with a carbon dioxide emission limit; comparing a current nitrogen oxide emission level of the geographical area with a nitrogen oxide emission limit; comparing a current particulate emission level of the geographical area with a particulate emission limit; comparing a current vehicle count or a current traffic density of the geographical area with a predetermined maximum vehicle number for the geographical area; or comparing a current noise level with a predetermined maximum noise level. The control system then communicates, at S540, the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter. There may be situations where the area-specific data is within an acceptable range but there may be social concerns that can be usefully addressed. Thus, in some embodiments, one or more predetermined social objectives may be input at S550 to the control system for consideration. These social objectives may comprise, with respect to a designated area, optimising fuel efficiency for the vehicle, minimising carbon dioxide emission, minimising nitrogen oxide emission, minimising particulate emission, minimising noise level, diverting traffic flow, or a combination thereof. For example, when all areaspecific data is within an acceptable range, it may be useful to reduce fuel consumption both for environmental concern and cost consideration, in which case the control system may determine a social objective to optimise fuel efficiency for the vehicle e.g. by instructing the control module of the vehicle to adjust the operation of the engine accordingly. In some situations, (the processor of) the control system may determine a plurality of control objectives, some objectives may contradict each other. Thus, in some embodiments, the method may further comprise checking at S560 whether more than one objective has been determined. If so (YES branch), the control system at S570 selects one control objective out of the plurality of control objectives according to a priority of one or more control objectives of the plurality of control objectives. For example, the area-specific data may simultaneously indicate that the NOx level and the particulate level are both high in the geographical area, and the control system may determine a control objective of reducing NOx level and a control objective of reducing particulate level. The control system may, for example, select the objective of reducing PM level if PM level has a higher priority for the specific geographical area, e.g. since the geographical area is a densely populated area and high PM level is more harmful to health. The priority of various control objectives may be predetermined and stored e.g. in a database accessible to the control system, such that whenever the control system determines that there are two or more control objectives based on the received area-specific data, the control system may refer to the predetermined priority to select the control objective with the highest priority. In some embodiments, the MLA may be trained to determine a respective priority for one or more control objective out of a plurality of control objectives, wherein, when the at least one control objective comprises a plurality of control objectives, determining a vehicle control parameter based on the at least one control objective may comprise the MLA determining a priority of one or more control objectives of the plurality of control objectives and selecting one control objective out of the plurality of control objectives according to the determined priority. In some embodiments, the at least one control objective may comprise reducing air pollutant level including carbon dioxide level, nitrogen oxide level, particulate level, and / or hydrocarbon level, increasing vehicle fuel efficiency, reducing traffic density within the geographical area, reducing noise level within the geographical area, and any combination thereof. Some control objectives may relate to the general air quality of the geographical area, some may relate to efficient traffic control, while some control objectives may relate to cost reduction (e.g. improving fuel economy). In some embodiments, the control system may determine a vehicle control parameter by determining a setting for an engine control parameter of the vehicle, the engine control parameter being a parameter that controls operation of the vehicle's engine. The engine control parameter may comprise a timing of diesel particulate filter regeneration cycle, an amount of exhaust gas to be recycled, a frequency or occurrence of exhaust gas regeneration cycle, a fuel injection rate, a fuel injection amount, or a combination thereof. In some embodiments, the control system may communicate the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter by instructing the vehicle's control module to operate exhaust gas regeneration to reduce a nitrogen oxide emission, or to halt exhaust gas regeneration to control particulate emission. In some embodiments, the control system may communicate the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter by instructing the vehicle's control module to increase fuel injection rate and / or fuel injection amount to reduce particulate emission, or to reduce fuel injection rate and / or fuel injection amount to reduce a nitrogen oxide emission. In some embodiments, the area-specific data may comprise an indication of whether the geographical area is a designated zone, and the operation instruction may comprise: halting a scheduled diesel particulate filter regeneration cycle to reduce particulate emission in the geographical area when the geographical area is not a designated zone; or advancing a scheduled diesel particulate filter regeneration cycle to clean the diesel particulate filter when the geographical area is a designated zone. In some embodiments, the area-specific data may comprise: air quality data; emission data including one or more of carbon dioxide emission level, nitrogen oxide emission level, particulate emission level; traffic flow data including one or more of vehicle count traffic density, traffic speed, traffic direction; noise level; population density; or a combination thereof. The control system may be arranged to communicate with the traffic infrastructure within the geographical area to enable active traffic control. Thus, in some embodiments, the control system may be arranged to send an instruction to one or more traffic infrastructure to modify operation thereof based on the at least one control objective, the instruction may comprise one or more of: altering a duration of one or more traffic lights to control a flow of traffic; lifting a barrier to one or more restricted public transport lanes to divert traffic onto the one or more restricted public transport lanes; removing one or more emission control zones to redistribute traffic; changing one or more road signs to indicate lifting of restrictions against use of a public transport lane; changing one or more road signs to vary a speed limit on one or more road sections; changing one or more road signs to instruct drivers of hybrid vehicles to switch to electric mode; and / or changing one or more road signs to instruct drivers to switch to an ECO mode. As will be appreciated by one skilled in the art, the present techniques may be embodied as a system, method or computer program product. Accordingly, the present techniques may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present techniques may take the form of a computer program product embodied in a computer readable medium having computer readable program code embodied thereon. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Computer program code for carrying out operations of the present techniques may be written in any combination of one or more programming languages, including object-oriented programming languages and conventional procedural programming languages. For example, program code for carrying out operations of the present techniques may comprise source, object or executable code in a conventional programming language (interpreted or compiled) such as C, or assembly code, code for setting up or controlling an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), or code for a hardware description language such as VerilogTM orVHDL (Very high-speed integrated circuit Hardware Description Language). The program code may execute entirely on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network. Code components may be embodied as procedures, methods or the like, and may comprise sub-components which may take the form of instructions or sequences of instructions at any of the levels of abstraction, from the direct machine instructions of a native instruction set to high-level compiled or interpreted language constructs. It will also be clear to one of skill in the art that all or part of a logical method according to the preferred embodiments of the present techniques may suitably be embodied in a logic apparatus comprising logic elements to perform the steps of the method, and that such logic elements may comprise components such as logic gates in, for example a programmable logic array or application-specific integrated circuit. Such a logic arrangement may further be embodied in enabling elements for temporarily or permanently establishing logic structures in such an array or circuit using, for example, a virtual hardware descriptor language, which may be stored and transmitted using fixed or transmittable carrier media. The examples and conditional language recited herein are intended to aid the reader in understanding the principles of the present technology and not to limit its scope to such specifically recited examples and conditions. It will be appreciated that those skilled in the art may devise various arrangements which, although not explicitly described or shown herein, nonetheless embody the principles of the present technology and are included within its scope as defined by the appended claims. Furthermore, as an aid to understanding, the following description may describe relatively simplified implementations of the present technology. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity. In some cases, what are believed to be helpful examples of modifications to the present technology may also be set forth. This is done merely as an aid to understanding, and, again, not to limit the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and a person skilled in the art may make other modifications while nonetheless remaining within the scope of the present technology. Further, where no examples of modifications have been set forth, it should not be interpreted that no modifications are possible and / or that what is described is the sole manner of implementing that element of the present technology. Moreover, all statements herein reciting principles, aspects, and implementations of the technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether they are currently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present technology. Similarly, it will be appreciated that any flowcharts, flow diagrams, state transition diagrams, pseudo-code, and the like represent various processes which may be substantially represented in computer-readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. The functions of the various elements shown in the figures, including any functional block labeled as a "processor", may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" or "controller" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and / or custom, may also be included. Software modules, or simply modules which are implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating performance of process steps and / or textual description. Such modules may be executed by hardware that is expressly or implicitly 5 shown. It will be clear to one skilled in the art that many improvements and modifications can be made to the foregoing exemplary embodiments without departing from the scope of the present techniques.

Claims

26 09 241. A computer-implemented method of controlling operation of a vehicle within a geographical area by a control system, the vehicle comprising a control module 5 and an internal combustion engine, the control module capable of controlling operation of the vehicle, and the control system being configured to communicate with the control module and with one or more data collection devices deployed within the geographical area, the method comprising:receiving area-specific data from the one or more data collection devices;10 determining at least one control objective based on the received areaspecific data;determining a vehicle control parameter based on the at least one control objective; andcommunicating the vehicle control parameter to the control module of the 15 vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter, wherein the area-specific data comprises an indication of whether the geographical area is a designated zone, and the operation instruction comprises:halting a scheduled diesel particulate filter regeneration cycle to reduce 20 particulate emission in the geographical area when the geographical area is not a designated zone; oradvancing a scheduled diesel particulate filter regeneration cycle to clean the diesel particulate filter when the geographical area is a designated zone.25 2. The method of claim 1, wherein determining at least one control objectivebased on the received area-specific data and / or determining a vehicle control parameter based on the at least one control objective is performed by a machine learning algorithm, MLA, executing on the control system, the MLA having been previously trained to determine a control objective based on the area-specific data30 and / or determine a vehicle control parameter based on a control objective.

3. The method of claim 1 or 2, wherein the at least one control objective comprises a plurality of control objectives, and determining a vehicle control parameter based on the at least one control objective comprises selecting one 35 control objective out of the plurality of control objectives according to a priority ofone or more control objectives of the plurality of control objectives.26 09 244. The method of claim 2, wherein the MLA is trained to determine a respective priority for one or more control objective out of a plurality of control objectives, 5 wherein, when the at least one control objective comprises a plurality of control objectives, determining a vehicle control parameter based on the at least one control objective comprises the MLA determining a priority of one or more control objectives of the plurality of control objectives and selecting one control objective out of the plurality of control objectives according to the determined priority.

105. The method of any preceding claim, wherein the at least one control objective comprises reducing air pollutant level including carbon dioxide level, nitrogen oxide level, particulate level, and / or hydrocarbon level, increasing vehicle fuel efficiency, reducing traffic density within the geographical area, reducing 15 noise level within the geographical area, and any combination thereof.

6. The method of any preceding claim, wherein determining a vehicle control parameter comprises determining a setting for an engine control parameter of the vehicle, the engine control parameter being a parameter that controls operation 20 of the internal combustion engine.

7. The method of claim 6, wherein the engine control parameter comprises a timing of diesel particulate filter regeneration cycle, an amount of exhaust gas to be recycled, a frequency or occurrence of exhaust gas regeneration cycle, a fuel 25 injection rate, a fuel injection amount, or a combination thereof.

8. The method of any preceding claim, wherein communicating the vehicle control parameter to the control module of the vehicle to coordinate operation of the vehicle in conformance with the vehicle control parameter comprises 30 instructing the control module to:operate exhaust gas regeneration to reduce a nitrogen oxide emission; or halt exhaust gas regeneration to control particulate emission.

9. The method of any preceding claim, wherein communicating the vehicle 35 control parameter to the control module of the vehicle to coordinate operation of26 09 24the vehicle in conformance with the vehicle control parameter comprises instructing the control module to:increase fuel injection rate and / or fuel injection amount to reduce particulate emission; or5 reduce fuel injection rate and / or fuel injection amount to reduce a nitrogenoxide emission.

10. The method of any preceding claim, wherein the area-specific data comprises: air quality data; emission data including one or more of carbon dioxide 10 emission level, nitrogen oxide emission level, particulate emission level; traffic flow data including one or more of vehicle count traffic density, traffic speed, traffic direction; noise level; population density; ora combination thereof.

11. The method of any preceding claim, wherein determining at least one 15 control objective based on the received area-specific data comprises comparing the area-specific data with a corresponding reference value and setting the at least one control objective based on results of the comparison.

12. The method of claim 11, wherein comparing the area-specific data with a 20 corresponding reference value comprises one or more of:comparing a current carbon dioxide emission level of the geographical area with a carbon dioxide emission limit;comparing a current nitrogen oxide emission level of the geographical area with a nitrogen oxide emission limit;25 comparing a current particulate emission level of the geographical area witha particulate emission limit;comparing a current vehicle count or a current traffic density of the geographical area with a predetermined maximum vehicle number for the geographical area; or30 comparing a current noise level with a predetermined maximum noise level.

13. The method of any preceding claim, wherein the at least one control objective further comprises a predetermined social objective.35 14. The method of claim 13, wherein the predetermined social objective26 09 24comprises, with respect to a designated area, optimising fuel efficiency for the vehicle, minimising carbon dioxide emission, minimising nitrogen oxide emission, minimising particulate emission, minimising noise level, diverting traffic flow, or a combination thereof.

515. The method of any preceding claim, further comprising sending an instruction to one or more traffic infrastructure to modify operation thereof based on the at least one control objective, the instruction comprising one or more of: altering a duration of one or more traffic lights to control a flow of traffic;10 lifting a barrier to one or more restricted public transport lanes to diverttraffic onto the one or more restricted public transport lanes;removing one or more emission control zones to redistribute traffic;changing one or more road signs to indicate lifting of restrictions against use of a public transport lane;15 changing one or more road signs to vary a speed limit on one or more roadsections;changing one or more road signs to instruct drivers of hybrid vehicles to switch to electric mode; and / orchanging one or more road signs to instruct drivers to switch to an ECO 20 mode.

16. The method of any preceding claims, wherein the data collection devices comprise one or more traffic cameras, one or more close circuit televisions, one or more sound recognition devices, one or more car counters, one or more air 25 quality sensors, and / or one or more tolling gates.

17. The method of any preceding claims, wherein the vehicle comprises a hydrogen injection unit configured to generate hydrogen through electrolysis and introduce the generated hydrogen to the internal combustion engine, and the 30 method further comprises determining the vehicle control parameter to be operation of the hydrogen injection unit.

18. A computer-readable storage medium comprising machine-readable code which, when executed by a processor, causes the processor to perform the method 35 of any one of claims 1 to 17.26 09 2419. A system for controlling operation of a vehicle within a geographical area, the vehicle comprising a control module and an internal combustion engine, the control module capable of controlling operation of the vehicle comprising:5 at least one processor;communication interface configured to communicate with the control module on the vehicle and with one or more data collection devices deployed within the geographical area; anda non-transitory computer-readable medium comprising machine-readable10 software codes, which, when executed by the at least one processor, causes the at least one processor to:receive, via the communication interface, area-specific data from the one or more data collection devices;determine at least one control objective based on the received area-specific 15 data;determine a vehicle control parameter based on the at least one control objective; andcommunicate, via the communication interface, the vehicle control parameter to the control module of the vehicle to coordinate operation of the 20 vehicle in conformance with the vehicle control parameter, wherein the areaspecific data comprises an indication of whether the geographical area is a designated zone, and the operation instruction comprises:halting a scheduled diesel particulate filter regeneration cycle to reduce particulate emission in the geographical area when the geographical area is not a 25 designated zone; oradvancing a scheduled diesel particulate filter regeneration cycle to clean the diesel particulate filter when the geographical area is a designated zone.

20. The system of claim 19, wherein the machine-readable software codes are 30 arranged for executing a machine learning algorithm, MLA, having been previously trained to determine a control objective based on the area-specific data and / or determine a vehicle control parameter based on a control objective.

21. The system of claim 19 or 20, wherein the communication interface is 35 further configured to communicate with one or more traffic infrastructure, the oneor more traffic infrastructure comprising one or more of traffic lights, one or more traffic barriers, one or more road signs, or a combination thereof.

22. The system of any of claims 19 to 21, further comprising one or more of 5 the data collection devices.

23. The system of any of claims 19 to 22, wherein the data collection devices comprise one or more traffic cameras, one or more close circuit televisions, one or more sound recognition devices, one or more car counters, one or more air 10 quality sensors, and / or one or more tolling gates.

24. A hydrogen injection unit provided to a vehicle comprising a control module and an internal combustion engine, the hydrogen injection unit being configured to generate hydrogen through electrolysis and introduce the generated hydrogen 15 into the internal combustion engine of the vehicle, wherein operation of the hydrogen injection unit is controlled by the control module in communication with the system of any of claim 19 to 23 under instruction of the system.26 09 24

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