Safety systems and procedures for vehicles

Abstract

Device for use with a vehicle (100), wherein the device (102) comprises: at least one sensor (112) for measuring a first biometric of a first occupant of the vehicle (100) and a second biometric of a second occupant of the vehicle (100); at least one memory (228); and a processor (226) for executing instructions for: determining a first occupant emotion of the first occupant based on the first biometric, determining a second occupant emotion of the second occupant based on the second biometric, and providing a suggestion within the vehicle (100) based on the first occupant emotion and the second occupant emotion, wherein the at least one sensor (112) is positioned on a door handle of the vehicle (100).

Description

Technical field The embodiments described here generally relate to autonomous vehicles. In particular, the disclosed embodiments relate to safety systems and procedures for autonomous vehicles (hereinafter referred to as autonomous vehicles). General state of the art Autonomous (self-driving) cars are equipped with numerous safety systems designed to respond precisely to obstacles, problems, and emergency situations. These systems rely on direct input data collected from the environment using onboard sensors. These currently available safety systems and this approach of collecting and processing direct input data from the environment represent an effective solution and work well for traffic when all vehicles are self-driving. However, these systems and this approach do not adequately account for a mixed environment with human participants (drivers) who do not necessarily follow or adhere to strict algorithms and rules in the same way as autonomous cars. The currently available safety systems and procedures for autonomous cars cannot predict or anticipate what other human road users will do.However, human occupants of a vehicle (e.g., a driver and / or other passengers) can sometimes intuitively analyze a dangerous situation and react before anything happens. For example, a human driver of another vehicle might be distracted by talking on the phone. From a purely mathematical perspective, this poses no problem, and the safety systems of an autonomous car might lack the basis or capability to detect such a problem, although a problem could arise within seconds. As another example, a human driver of another car might be approaching a roundabout and, based on speed, direction, focus, or other factors, might appear as if they are not going to stop and yield to other vehicles entering the roundabout. On the other hand, from a purely mathematical perspective there may be sufficient time to brake or decelerate, although the currently available safety systems for autonomous cars may not have a basis or capability to detect the driver's intention through the roundabout. Autonomous cars also introduce a new driving experience, controlled by a machine rather than a human operator. This shift in control can offer an experience that differs for a given occupant and is likely to be less comfortable, depending on the occupant's driving preferences and / or driving style. Currently available autonomous control systems and procedures can deliver a mechanistic experience determined solely by algorithms based on sensor data inputs—an experience that does not take into account occupant preferences and perceptions regarding driving aspects. DE 10 2004 020 255 A1 describes a monitoring system for vehicles, with an electronic camera and an associated image processing unit, in which the image data captured by the camera are evaluated and vehicle occupants at individual seats are recorded and biometrically identified, characterized in that a storage device is provided from which personal data can be retrieved for such an identified vehicle occupant, which can be made available to vehicle functions. DE 10 2013 001 878 A1 describes a method for controlling or regulating an air conditioning and / or ventilation device of a vehicle, wherein an image of at least a part of a vehicle interior is generated by means of an image acquisition device, wherein at least one climatic information parameter is determined on an image-based basis, wherein at least one control or regulation parameter of the air conditioning and / or ventilation device is determined as a function of the at least one climatic information parameter, and a device for controlling or regulating an air conditioning and / or ventilation device. DE 10 2013 001 868 A1 relates to a method for operating a motor vehicle in which at least one gesture of an occupant of the motor vehicle is detected by means of a gesture detection device and compared with at least one predetermined target gesture, wherein at least one function of the motor vehicle is activated or terminated depending on the comparison of the detected gesture with the target gesture, wherein the function is activated or terminated depending on at least one signal characterizing an interaction between the occupant and at least one other occupant of the motor vehicle. DE 10 2005 047 137 A1 relates to an occupant protection and / or comfort system for a vehicle with several adaptive protection system or comfort components, each assigned to a vehicle seat. These components can be adjusted using a stored safety and / or comfort profile specific to a person, when the person is identified by means of a personal identification device for capturing biometric identification characteristics from body parts of the occupant. It is proposed that the personal identification device include a biometric sensor for identifying the occupant and that means for the unambiguous assignment of the occupant to a vehicle seat be provided. DE 10 2012 216 869 A1 describes a method for adapting a vehicle restraint system to the situation, comprising: determining a biometric characteristic, in particular the height, geometry or proportions of an occupant of the vehicle, and adapting the restraint system to the determined biometric characteristic. DE 198 01 009 C1 describes a method for braking a vehicle, wherein sensors, preferably arranged on the driver's wrists or on the steering wheel rim, detect changes in body reactions indicating emergency or stressful situations and initiate an automatic braking process depending on this, wherein the automatically initiated braking process is maintained only if and for as long as a change in the operation of the vehicle indicating a braking process, preferably a change in the accelerator and / or brake pedal position and / or a change in the position of a foot of the driver operating the accelerator and brake pedal, is additionally detected by means of a detection and actuation device. Summary of the invention The problem underlying the invention is solved by the subject matter of the independent claims. Further advantageous embodiments are specified in the dependent claims. Brief description of the drawings Fig. 1A is a partial side view of a vehicle incorporating a control system based on occupant parameters, according to one embodiment. Fig. 1B is a partial elevation view of vehicle 1A. Fig. 2 is a schematic diagram of a control system based on occupant parameters, according to one embodiment. Fig. 3 is a flowchart of a method for controlling an autonomous vehicle based on occupant parameters, according to one embodiment. Detailed description of preferred embodiments Currently available autonomous vehicles operate according to rigid standards, strictly adhering to algorithms and rules. Generally, the vehicles detect and react to external data, but do not consider or react to the behavior of passengers inside the vehicle in the absence of external sensor data (which might indicate danger). Many situations are "legally OK" from a traffic data perspective, but could very quickly escalate into dangerous situations, such as: drivers turning without activating turn signals or suddenly swerving; distracted drivers approaching an intersection, junction, or roundabout; a large vehicle (e.g., a truck) approaching at very high speed; and someone changing a tire on their car at the side of the road, and someone else overtaking your vehicle at the exact point where you are passing the parked vehicle and the unprotected driver. There are many other similar situations. The present disclosure provides systems and methods for controlling an autonomous vehicle. The disclosed systems and methods take into account occupant parameters, including reactions, sensations, preferences, patterns, history, context, biometrics, feedback, and the like, to provide suggested driving aspects to the autonomous vehicle or otherwise to guide or control driving aspects of the autonomous vehicle in order to improve the safety and / or comfort of an autonomous driving experience. The disclosed embodiments can include sensors that would track the occupants inside the vehicle. A single occupant, whom the embodiments identify as the "human driver," can be tracked, even if that person is not actively participating in the journey. Alternatively or additionally, all passengers can be tracked. The disclosed embodiments can monitor certain occupant parameters. If an anomaly occurs in one or more of these parameters, the system can perform a human-like defensive action without compromising the built-in safety of the autonomous vehicle.Example actions may include the following: slowing down while inside the junction or roundabout to prevent a potential collision; in right-hand traffic countries, moving to the right side when the driver sees another car swerving out of its lane and about to hit its car; slowing down early and signaling with hazard warning lights when a sudden traffic jam is detected on a motorway; slowing down when someone is seen driving recklessly, swerving wildly, etc.; other defensive measures, which usually involve reducing speed and increasing following distance. The disclosed embodiments may include sensors and other information sources to detect human sensations regarding driving aspects and to provide suggested driving aspects according to these sensations. Exemplary embodiments are described below with reference to the accompanying drawings. Many different forms and embodiments are possible without deviating from the essence and teachings of the invention, and therefore the disclosure should not be interpreted as being limited to the exemplary embodiments presented here. Rather, these exemplary embodiments are provided to ensure that this disclosure is thorough and complete and fully conveys the scope of protection of the disclosure to those skilled in the art. In the drawings, the sizes and relative sizes of components may be exaggerated for the sake of clarity. The terminology used here serves only the purpose of describing specific exemplary embodiments and is not intended to be restrictive. As used here, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.It is further understood that the terms "comprise" and / or "comprehensive," when used in this patent specification, specify the presence of the listed features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Unless otherwise specified, a range of values, when indicated, includes both the upper and lower bounds of the range as well as any subranges in between. Figures 1A and 1B illustrate an autonomous vehicle 100 that includes a system 102 for occupant-parameter-based control, according to an embodiment of the present disclosure. In particular, Figure 1A is a partial side view of the vehicle 100. Figure 1B is a partial top view of the vehicle 100. With general and collective reference to Figures 1A and 1B, the vehicle 100 can be fully autonomous, such that it is capable of driving itself to an intended destination without active intervention from a human operator. The vehicle 100 can exhibit any level of partial autonomy, allowing a human operator to monitor and / or control aspects of driving, and enabling the vehicle 100 to assume control of aspects of driving (e.g., steering, braking, signaling, acceleration, etc.) at certain times or under certain circumstances. The vehicle 100 can, among other things, use artificial intelligence, sensors, or global positioning system coordinates to drive itself autonomously or to assume control of aspects of driving.The vehicle 100 includes the occupant parameter-based control system 102, an autonomous vehicle control unit 110, one or more sensors 112a, 112b, 112c, 112d, 112e, 112f, 112g (collectively 112), and a network interface 118. In other embodiments, the occupant parameter-based control system 102 may include the autonomous vehicle control unit 110 and / or the one or more sensors 112 and / or the network interface 118. The occupant-parameter-based control system 102 can include an occupant monitoring system for obtaining occupant data for an occupant of the autonomous vehicle 100, a learning engine for processing the occupant data to identify one or more proposed driving aspects based on the occupant data, and a vehicle interface for communicating the proposed driving aspects to the autonomous vehicle 100. These system elements are shown in Fig. 2 and are described in more detail below with reference to the same figure. The occupant monitoring system can include one or more sensors 112 or be otherwise coupled to them. The one or more sensors 112 may include a microphone 112a, an interior visual imaging system 112b, an exterior visual imaging system 112c, and one or more pressure sensors 112d, 112e, 112f, 112g. The one or more pressure sensors 112 may detect and / or monitor one or more occupant parameters, which are used by the system 102 to control the identification of one or more suggested driving aspects. For example, one or more sensors can detect and / or monitor 112 occupant parameters that indicate an occupant's reaction to a potential hazard outside the autonomous vehicle. The sensors can detect and monitor occupant parameters such as sudden muscle tension or contraction, sudden backward movement of the occupant toward a seat back, twitching of at least one or both feet, speech use (or other voice use, such as shouting), eye movement, pupil dilation, head movement, heart rate, respiratory rate, and changes in air intake (e.g., air intake volume), any or several of which are natural reactions or responses of an occupant observing the external environment and intuitively (e.g.,Based on experience (recognizing a distracted state of a human driver of another vehicle), the system predicts or anticipates a potential hazardous situation and / or resulting damage, such as that caused by a collision. The control system 102 (e.g., a machine learning system) can process sensor data from the one or more sensors 112 of the occupant monitoring system and detect a potential hazard outside the autonomous vehicle 100 based on one or more occupant parameters. In this way, the control system 102 can provide a human-machine interface that allows the autonomous vehicle 100 and / or the autonomous vehicle control system 110 to consider occupant parameters. As another example, one or more sensors can collect 112 occupant data relating to occupant parameters that can be used to detect an occupant's sensation 10. The sensors can detect and monitor such occupant parameters as speech, tone of voice, biometrics (e.g., pulse rate and blood pressure), occupant image data (e.g., for use in emotion extraction procedures), and responses and / or commands (e.g., a feedback mechanism to provide a way for the user to express like / dislike) via voice and / or a graphical user interface 120 (e.g., a touchscreen). Some examples of sensor applications include the following. The pressure sensors 112g in a steering wheel 20, the door handle(s), and other occupant handles can detect and monitor occupant parameters, such as sudden muscle tension or contraction. The pressure sensors 112d and 112e in a seat 22 (e.g., the pressure sensor 112d in the backrest and / or the pressure sensor 112e in the seat base) can detect occupant parameters, such as a sudden backward movement of the occupant toward a backrest. A sensor 112f in the floor can detect occupant parameters, such as a twitch of at least one foot. The microphone 112a can detect occupant parameters, such as voice commands, occupant speech, occupant use of speech forms, and / or tone of voice. Occupant speech and / or speech forms can include commands, phrases, profanity, and other speech usage. Other sensors can detect biometrics, such as pulse rate and blood pressure. The interior vision imaging system 112b can detect occupant parameters such as eye movement, pupil dilation, and head movement. More specifically, the interior vision imaging system 112b captures image data of the occupant 10 (or a plurality of occupants) of the vehicle 100. The interior vision imaging system 112b can include an image sensor or a camera for capturing images of the occupant 10. In certain embodiments, the interior vision imaging system 112b can include one or more array cameras. The image data captured by the interior vision imaging system 112b can be used for various purposes. The image data can be used to identify the occupant 10 and to obtain information about the occupant 10, such as a typical head position, health information, and other contextual information. Alternatively or additionally, the image data can be used to detect a position (e.g.,The interior vision imaging system 112b can use the head / eye position (height, depth, lateral distance) of occupant 10, which in turn can be used to detect and / or track the current gaze direction of occupant 10. The system can include an eye movement tracker for monitoring an eye movement parameter of occupant 10. The eye movement tracker can include a gaze direction tracker for processing occupant image data of occupant 10 from the autonomous vehicle 100 to determine the current area of ​​central vision of occupant 10. The interior vision imaging system 112b can include a pupil monitor for monitoring pupil dilation, wherein the pupil monitor includes a pupil tracker for processing occupant image data of occupant 10 from the vehicle 100 to determine the pupil size of occupant 10.The Interior Visual Imaging System 112b can also provide occupant image data that can be used in emotion extraction procedures to identify one or more occupant sensations. The external view image acquisition system 112c captures image data of an environment in front of the vehicle 100, which can help in collecting occupant data and / or parameters relating to what the occupant 10 might be focusing on. The image data captured by the external view image acquisition system 112c can be processed for gaze direction tracking and / or line-of-sight detection to identify what the occupant 10's attention is focused on (e.g., a driver of another vehicle talking on a mobile phone and being inattentive, or a skateboarder about to dive into traffic). The external view image acquisition system 112c may include an image sensor or camera to capture images of an area outside the vehicle 100. The external view image acquisition system 112c may include multiple image sensors at different angles to capture multiple perspectives.The external vision imaging system 112c can also include several types of image sensors, such as active infrared image sensors and visible light spectrum image sensors. Generally, the external vision imaging system 112c captures images of an area in front of the vehicle 100 or ahead of the vehicle 100 in a direction of travel. In certain embodiments, the external vision imaging system 112c can include one or more array cameras. The image data acquired by the external vision imaging system 112c can primarily be used by the autonomous vehicle control unit 110 to guide and control the navigation of the autonomous vehicle 100. With particular reference to Fig. 1B, the line of sight 152 of the occupant 10 can be determined by an eye-tracker of the interior vision system 112b. Using the line of sight 152 and external image data obtained from the exterior vision system 112c, the system 102 can determine an occupant's focus of attention. In Fig. 1B, the line of sight 152 of the occupant 10 is directed towards a traffic sign 12. It is understood that under other circumstances, the occupant 10 may be focused on a driver of another vehicle who may be inattentive or distracted by a mobile phone or other mobile device, or on a pedestrian (e.g., a small child, walker, jogger, skateboarder, motorcyclist, or the like) who is inattentive, gets dangerously close to traffic, or otherwise moves very close to the autonomous vehicle 100, if it is moving. The control system 102 can be a safety system for the autonomous vehicle 100, providing one or more suggested driving aspects that include one or more defensive actions to improve the safety of the occupants of the autonomous vehicle 100. For example, a human driver of another vehicle might be distracted by talking on their phone. The occupant 10 of the autonomous vehicle 100 might watch with concern as the other vehicle approaches an intersection faster than expected. The occupant 10 might tighten their grip on a handrail or the steering wheel 20 and stiffen in anticipation of a potential impact against the seat 22. The system 102 receives sensor data for one or more of these occupant parameters and can report the potential hazard and / or a suggested defensive action to the autonomous vehicle control system 110, for example, to improve the safety of the occupant 10.Examples of defensive actions that can improve occupant safety include: reducing the speed of the autonomous vehicle 100; signaling and / or activating the hazard warning lights; tightening the seat belts; closing the windows; locking the doors; unlocking the doors; increasing the distance between the autonomous vehicle 100 and vehicles in its vicinity; alerting the authorities; altering the current route; altering the stopping distance; sounding an audible alarm; and activating one or more emergency sensors designed to detect potential hazards, so that these emergency sensors provide additional input to the autonomous vehicle control system 110. In this way, the control system 102 can provide a human-machine interface that delivers a superior additional decision vector for a limited set of instructions. The control system 102 can also provide one or more suggested driving aspects based on one or more occupant sensations and / or other occupant data to deliver an enhanced driving experience for the occupant(s). In other words, the control system 102 can be a system for suggesting driving aspects to the autonomous vehicle 100, and the suggested driving aspects can enable the vehicle 100 to deliver an adaptive driving experience by taking into account one or more occupant sensations, preferences, driving patterns, and / or additional context, thereby aiming for a more personalized and / or tailored driving experience. The machine (i.e., the vehicle 100) can then drive in such a way that the occupants can expect to experience a ride similar to having their hand on the "steering wheel" (e.g., controlling the vehicle 100) or as if the "steering wheel" were in their hands.System 102 can use one or more sensations, driving history, context, and / or preferences to suggest or even control aspects of the driving experience, such as speed, acceleration, path (e.g., sharpness of curves, route), and the like, to personalize the driving experience and adapt to the occupant's needs and / or preferences. In this way, System 102 can provide a human-machine interface for control, offering a superior additional decision vector for a limited set of instructions. System 102 enables the autonomous vehicle 100 to function and operate according to the occupant's emotions and intentions, rather than simply driving in a robotic manner based on sensation. Network interface 118 is designed to receive occupant data from sources outside and near the vehicle 100. Network interface 118 can be equipped with conventional network connectivity, such as Ethernet (IEEE 802.3), Token Ring (IEEE 802.5), Fiber Distributed Datalink Interface (FDDI), or Asynchronous Transfer Mode (ATM). Furthermore, the computer can be configured to support a variety of network protocols, such as Internet Protocol (IP), Transfer Control Protocol (TCP), Network File System over UDP / TCP, Server Message Block (SMB), Microsoft® Common Internet File System (CIFS), Hypertext Transfer Protocols (HTTP), Direct Access File System (DAFS), File Transfer Protocol (FTP), Real-Time Publish Subscribe (RTPS), Open Systems Interconnection (OSI) protocols, Simple Mail Transfer Protocol (SMTP), Secure Shell (SSH), Secure Socket Layer (SSL), etc. The network interface 118 can provide an interface to wireless networks and / or other wireless communication devices. For example, the network interface 118 can enable wireless connectivity with wireless sensors (e.g., biometric sensors for obtaining an occupant's pulse rate, blood pressure, temperature, etc.), a mobile phone or handheld device of an occupant, or a wearable device (e.g., wrist activity tracker, Apple® Watch). As another example, the network interface 118 can establish a wireless data connection with a wireless network access point 140 located outside the vehicle 100. The network interface 118 can connect to a wireless network access point 140 that is coupled to a network, such as a local area network (LAN), a wide area network (WAN), or the internet.In certain embodiments, the wireless network access point 140 is located on or coupled to a geographically localized network that is isolated from the internet. These wireless connections to other devices and / or networks via the network interface 118 make it possible to obtain occupant data, such as calendar and / or schedule information from the occupant's calendar. Contextual data can also be obtained, such as statistics on driving aspects (e.g., speed, acceleration, turning radius, driving patterns, routes) of other vehicles through a given sector or geographical area, medical information of the occupant, significant current events (such as those that might affect an occupant's mood), and other contextual data that may be helpful in determining proposed driving aspects for the autonomous vehicle 100. In certain embodiments, the wireless network access point 140 is coupled to a "cloudlet" of a cloud-based distributed computing network. A cloudlet is a computer architecture element that represents an intermediate stage (e.g., mobile device – cloudlet – cloud). Cloudlets are decentralized and widely dispersed internet infrastructure whose compute cycles and storage resources can be accessed by nearby mobile computers. A cloudlet can be viewed as a local "data center" designed and configured to bring a cloud-based distributed computing architecture or network closer to a mobile device (e.g., in this case, the autonomous vehicle control unit 110 or the system 102) and to provide compute cycles and storage resources accessible to nearby mobile devices.A cloudlet may only have a soft state, meaning it has no hard state, although it can contain a cached state from the cloud. It can also buffer data en route from one or more mobile devices to the cloud's security. A cloudlet may have sufficient computing power (i.e., CPU, RAM, etc.) to offload resource-intensive calculations from one or more mobile devices. The cloudlet may have excellent connectivity to the cloud (typically a wired internet connection) and is generally not limited by finite battery life (i.e., it is connected to a power source). A cloudlet is logically close to the associated mobile devices. "Logical proximity" translates to low endpoint-to-endpoint latency and high bandwidth (i.e., one-hop Wi-Fi). Logical proximity can imply physical proximity.A cloudlet is self-managing, requiring little more than power, internet connectivity, and access control or setup. Its ease of management can be compared to a household appliance model of computing resources, allowing for trivial deployment in business premises such as a café or doctor's office. Internally, a cloudlet can be viewed as a cluster of multi-core computers with internal gigabit connectivity and a high-bandwidth wireless LAN. In certain embodiments, the wireless network access point 140 is coupled to a fog of a cloud-based distributed computing network. A fog can be more extensive than a cloudlet. For example, a fog could provide computing power from ITS (Intelligent Transportation Systems) infrastructure along the road: e.g., uploading / downloading data at a smart intersection. The fog can be restricted to peer-to-peer connections along the road (i.e., not transmitting data to the cloud or a remote data center), but would extend along the entire highway system, and the vehicle can connect to and disconnect from local "fog" computing along the road. A fog can also be described as a distributed associated network of cloudlets. As another example, a fog can provide distributed computing across a collection of parking meters, where each meter can be an edge of the fog and establish a peer-to-peer connection with a vehicle. The vehicle can travel through a "fog" of edge computation provided by each parking meter. In certain other embodiments, the network interface 118 can receive occupant data from a satellite (e.g., a Global Positioning System (GPS) satellite, XM radio satellite). In certain other embodiments, the network interface 118 can receive occupant data from a cell tower. It is understood that other suitable wireless data connections are possible. Figures 1A and 1B illustrate a single occupant seated in a typical driver's position in a vehicle. It is understood that the system 102 can monitor additional or other occupants, such as those typically seated where a front passenger and / or a rear passenger would sit. In other words, the autonomous vehicle 100 may not have a steering wheel 20, but instead a simple handgrip, and therefore may not have a driver's seat or position. Furthermore, the system 102 can monitor a multitude of occupants and can monitor suggested driving aspects based on a multitude of occupants (e.g., all occupants in the vehicle). Fig. 2 is a schematic diagram of a system 200 for control based on occupant parameters, according to one embodiment. The system 200 includes a processing device 202, an interior image acquisition system 212b, an exterior image acquisition system 212c, one or more sensors 212 alternatively to or in addition to the image acquisition systems 212b, 212c, and / or an autonomous vehicle control unit 210 for controlling navigation and other driving aspects of an autonomous vehicle. The processing device 202 can be similar to or analogous to the system 102 for control based on occupant parameters from Fig. 1A and Fig. 1B. The processing device can include one or more processors 226, a memory 228, input / output interfaces 216, and a network interface 218. Memory 228 can contain information and instructions necessary for implementing various components of system 200. For example, memory 228 can contain various modules 230 and program data 250. As used here, the word "module," whether in uppercase or lowercase, refers to logic that may be implemented in hardware or firmware, or to a collection of software instructions that may have input and output points and are written in a programming language, such as C++. A software module may be compiled and linked into an executable program, which may be contained in a dynamic link library, or it may be written in an interpreted language, such as BASIC. A software module or program may be in an executable state, or referred to as an executable. An "executable" generally means that the program is capable of running on the computer system without the involvement of a computer language interpreter.The term "automatic" generally refers to an operation that occurs without significant user intervention or with only limited user intervention. The term "start" generally refers to initiating the operation of a computer module or program. It is understood that software modules can be called by other modules or by themselves and / or invoked in response to detected events or interrupts. Software instructions may be embedded in firmware, such as an EPROM. Hardware modules may include interconnected logic units, such as gates and flip-flops, and / or may include programmable units, such as programmable gate arrays or processors. The modules can be implemented in hardware, software, firmware, and / or a combination thereof. For example, as shown, the modules 230 can include an occupant monitoring system 232, a gaze tracker 234, and a learning machine 236. The learning machine 236 can include a detection module 242, a sensation analyzer 244, and / or an occupant profiler 246. The modules 230 can handle various interactions between the processing device 202 and other elements of the system 200, such as the autonomous vehicle control unit 210 and the sensors 212 (including the imaging system 212b, 212c). Furthermore, the modules 230 can generate data that can be stored by the memory 228. For example, the modules 230 can generate program data 250, such as profile data sets 252, which may include correlations 254 between driving aspects 256 and occupant parameters 258. The occupant parameters may include sensations 262, biometrics 264, history 266, context 268, preferences 270, statistics 272, and the like. The occupant monitoring system 232 can assist in collecting occupant data to detect and / or monitor occupant parameters 258. The learning engine 236 can process the occupant data and / or the occupant parameters 258 to determine or identify suggested driving aspects 256 for communication with the autonomous vehicle via a vehicle interface (e.g., input / output interface 216) with the autonomous vehicle's control unit 210. The detection module 242 can process sensor data from one or more sensors 212, monitoring one or more occupant parameters to detect a potential hazard located outside the autonomous vehicle. Detection is achieved based on the occupant parameters 258. The sensation analyzer 244 processes occupant data and detects an occupant sensation 262 in relation to current driving aspects 256, which the sensation analyzer 244 records together with a correlation 254 of the occupant sensation 262 and the driving aspects 256. The occupant profiler 246 maintains an occupant profile that includes recorded correlations 254 of driving aspects 256 for the occupant and occupant parameters 258, including sensations 262, biometrics 264, history 266, context 268, preferences 270 and statistics 272. As previously explained, sensations 262 and biometrics 264 can be detected by one or more sensors 212 (including the internal vision imaging system 212b) and the detection module 242. Biometrics 264, history 266, context 268, preferences 270, and statistics 272 can be obtained through the network interface 218. The interior visual imaging system 212b is designed to acquire image data of an occupant of a vehicle in which the system 200 is mounted and / or operable. The interior visual imaging system 212b may include one or more image sensors or cameras for capturing images of the operator. In certain embodiments, the interior visual imaging system 212b may include one or more array cameras. The image data acquired by the interior visual imaging system 212b can be used to detect an occupant's response to a potential hazard, to detect an occupant's sensations, to identify an occupant, to detect an occupant's head / eye position, and to detect and / or track an occupant's current gaze direction. The external view image acquisition system 212c captures image data of an environment in front of a vehicle. The external view image acquisition system 212c may include one or more image sensors or cameras for capturing images of an area outside the vehicle, generally an area in front of the vehicle or in a direction of travel ahead of the vehicle. In certain embodiments, the external view image acquisition system 212c may include one or more array cameras. The image data captured by the external view image acquisition system 212c may be analyzed or otherwise used to identify objects in the environment around the vehicle (e.g., generally in front of the vehicle or in a direction of travel ahead of the vehicle) in order to collect occupant data. The gaze tracker 234 is designed to process occupant image data acquired by the interior vision imaging system 212b in order to determine the line of sight of a vehicle occupant's current gaze direction. The gaze tracker 234 can analyze the image data to detect the occupant's eyes and determine the direction in which the eyes are focused. The gaze tracker 232 can continuously process current occupant image data to detect and / or track the occupant's current gaze direction. In certain embodiments, the gaze tracker 232 can process the occupant image data essentially in real time. The gaze tracker may include a pupil monitor for monitoring pupil dilation.The pupil monitor may include a pupil tracker for processing occupant image data of a vehicle occupant in order to determine the size of the occupant's pupil. Driving Aspects 256 can include, but are not limited to, defensive actions such as deceleration, swerving, seatbelt tightening, closing windows, locking doors, unlocking doors, creating a greater distance (e.g., changing speed and / or direction), alerting authorities, changing the route, altering a stopping distance (e.g., braking harder for faster deceleration), providing audio alerts and signals (e.g., lights) to other vehicles, and activating emergency sensors (e.g., focusing a camera to follow a user's gaze) to determine potential hazards and to provide additional information / feedback to the autonomous vehicle's control system. Driving Aspects 256 can also include adjusting the autonomous vehicle's speed and / or acceleration and / or turning radius and / or route. Each of the sensations stored in memory 228 can be a determination of an occupant's behavior based on, for example, speech, biometrics, image processing, and live feedback, or represent it in some other way. Classical sensation analysis can analyze occupant sensations in relation to current driving aspects using standard text sensation analysis methods, whereas speech-to-text and / or acoustic models are used to identify sensation by means of the tone of voice. Biometrics 264 can be integrated into sensation analysis, such as by recording pulse rate, blood pressure, and / or temperature from one or more occupants, to understand distress levels as a result of the autonomous vehicle's actual driving experience. For example, sudden changes in biometrics 264 may signal distress based on a current aspect of the driving experience. Conversely, an occupant's biometric levels upon entering the vehicle can be used to detect other sensations. For example, biometric levels that are already above normal or typical for the occupant upon entering the vehicle may indicate stress, anxiety, or similar feelings. Image processing can include sensation extraction techniques to analyze occupant emotions as they are apparent from facial expressions, actions, and the like.Live feedback mechanisms can be used to explore and / or confirm likes and dislikes, detected sensations, moods, preferences, and the like. Driving History 266 can provide a representation of how an occupant typically drives when a vehicle is being controlled. An occupant's driving style can be a strong indicator of the driving experience they would want with an autonomous vehicle. For example, someone who takes sharp turns or drives as fast as legally possible would expect the same. Someone who extends their route to ensure safety or drives along the coast whenever possible would expect the autonomous vehicle to follow those same scenic routes. Driving History 266 can be obtained from a training vehicle or during a training period of occupant operation of the autonomous vehicle. Context 268 can include information such as the occupant's age, current medical condition, mood, and free time (e.g., according to a calendar or schedule) and can be important for determining appropriate driving aspects. For example, an elderly person with heart problems may not appreciate, or may even be adversely affected by, an autonomous vehicle taking sharp turns or always driving as fast as possible. Similarly, tourists as passengers may prefer a slightly longer route past significant or specific landmarks. Preferences 270 can be entered by an inmate via a graphical user interface or a client computing device that can provide data so that it can be accessed via a wireless network. A statistic 272 can be collected by the autonomous vehicle or can be captured by a network access point, as described above. If a majority of vehicles (e.g., 90%) passing through a given geographical sector follow similar driving characteristics (e.g., speed, acceleration, turning radius, or the like), this statistic can inform the determination of suggested driving characteristics for an autonomous vehicle. Fig. 3 is a flowchart of a method 300 for controlling an autonomous vehicle based on occupant parameters, according to one embodiment. Occupant data is recorded or otherwise received 302, such as from sensors, a wireless network connection, and / or a stored profile. The occupant data can help in identifying occupant parameters. The occupant data is processed 304 to identify 306 one or more suggested driving aspects based on the occupant data and / or the occupant parameters. Alternatively or additionally, a detected potential hazard can be communicated 308 to the autonomous vehicle.Processing occupant data and / or parameters may include identifying an occupant response, such as to a potential hazard outside the vehicle, in order to detect the potential hazard, and suggesting a driving aspect, such as a defensive action, in order to increase occupant safety. Processing occupant data and / or parameters can involve detecting occupant perception in relation to current driving aspects and recording a correlation between the detected occupant perception and the current driving aspect in an occupant profile. The occupant data / parameters can be processed to identify 306 suggested driving aspects based on a correlation in an occupant profile that correlates an occupant perception and a driving aspect. The suggested driving aspects include a suggested speed, a suggested acceleration, a suggested cornering technique, and / or a suggested route that the occupant might enjoy, as determined, for example, based on the occupant perception. Examples of implementation Examples may include items such as methods, means for carrying out actions of the methods, at least one machine-readable medium including instructions which, when carried out by a machine, cause the machine to carry out actions of the methods, or a device or system. Example 1. A safety system for an autonomous vehicle, wherein the system comprises: an occupant monitoring system for monitoring an occupant of the autonomous vehicle, wherein the occupant monitoring system comprises one or more sensors for monitoring one or more occupant parameters; a detection module for processing sensor data received from the one or more sensors of the occupant monitoring system.and for detecting a potential hazard outside the autonomous vehicle based on one or more occupant parameters; and a vehicle interface for communicating a detection of a potential hazard outside the autonomous vehicle to the autonomous vehicle, wherein the detection by the detection module is based on one or more occupant parameters. Example 2. The system of Example 1, wherein the occupant monitoring system is designed to monitor a plurality of occupants of the autonomous vehicle. Example 3. The system of any of Examples 1-2, wherein the occupant monitoring system is designed to monitor an occupant positioned in a driver's seat of the autonomous vehicle. Example 4. The system of any of Examples 1-3, wherein the occupant monitoring system is designed to monitor one or more occupant parameters,that indicate an occupant response to a potential hazard outside the autonomous vehicle. Example 5. The system according to Example 4, wherein the occupant monitoring system is designed to monitor one or more occupant parameters that indicate a human occupant response to a non-deterministic potential hazard outside the autonomous vehicle. Example 6. The system according to any of Examples 1-5, wherein the one or more occupant parameters include one or more of the following: sudden tensing or contraction of muscles; sudden backward movement of the occupant toward a seat back; twitching of at least one foot; speech; eye movement; pupil dilation; head movement; pulse rate; respiratory rate; and change in air intake. Example 7. The system according to any of Examples 1-6,where each sensor of the one or more sensors is intended to monitor one or more occupant parameters. Example 8. The system according to any of Examples 1-7, wherein the one or more sensors include one or more pressure sensors. Example 9. The system according to Example 8, wherein the one or more pressure sensors are arranged on handrails within a passenger compartment of the autonomous vehicle to detect that the occupant is tensing his or her hand muscles. Example 10. The system according to Example 8, wherein the one or more pressure sensors are arranged within a seat of the autonomous vehicle to detect occupant movement relative to the seat, including movement towards a seat backrest. Example 11. The system according to Example 8, wherein the one or more pressure sensors are arranged on a floor of a passenger compartment of the autonomous vehicle to detectthat the occupant twitches at least one foot. Example 12. The system of Example 8, wherein the one or more pressure sensors are arranged within a seat of the autonomous vehicle to detect a breathing rhythm. Example 13. The system of any one of Examples 1-12, wherein the one or more sensors include a microphone for detecting that the occupant is using speech. Example 14. The system of any one of Examples 1-13, wherein the one or more sensors include a microphone for detecting occupant speech. Example 15. The system of any one of Examples 1-14, wherein the one or more sensors include an eye-tracker for monitoring an eye-movement parameter of the occupant.wherein the eye-tracker comprises: a gaze direction tracker for processing occupant image data of the autonomous vehicle occupant to determine a current area of ​​the occupant's central view; and an interior view image acquisition system for acquiring occupant image data of the autonomous vehicle occupant for processing by the gaze direction tracker. Example 16. The system of Example 15, wherein the gaze direction tracker is designed to determine a line of sight of a current gaze direction of the autonomous vehicle occupant, to determine a field of view of the occupant based on the line of sight of the current gaze direction of the occupant, and to determine the current area of ​​the occupant's central view within the field of view. Example 17. System of Example 15, wherein the gaze direction tracker includes a pupil monitor for monitoring pupil dilation,wherein the pupil monitor includes a pupil tracker for processing occupant image data of a vehicle occupant to determine the occupant's pupil size. Example 18. The system according to any of Examples 1-17, wherein the vehicle interface communicates the detection of potential hazard to a controller of the autonomous vehicle. Example 19. The system according to any of Examples 1-8, wherein the vehicle interface communicates the detection of potential hazard to the autonomous vehicle by providing suggested driving aspects, including a defensive action, to enhance the safety of the autonomous vehicle's occupants. Example 20. The system according to Example 19,where the defensive action to increase safety is one of the following: reducing the speed of the autonomous vehicle; signaling with hazard warning lights; tightening the seat belts; closing the windows; locking the doors; unlocking the doors; increasing the distance between the autonomous vehicle and vehicles in its vicinity; alerting the authorities; altering the route; altering the stopping distance; sounding an audible warning signal; and activating one or more emergency sensors designed to detect potential hazards. Example 21. A method for controlling an autonomous vehicle, wherein the method comprises the following steps: receiving occupant data for an occupant of the autonomous vehicle; processing occupant data received from the occupant monitoring system;to identify one or more proposed driving aspects based on the occupant data; and communicate the one or more proposed driving aspects to the autonomous vehicle via a vehicle interface. Example 22. The method of Example 21, wherein the occupant data includes one or more occupant parameters indicating an occupant response to a potential hazard outside the autonomous vehicle, wherein processing occupant data includes detecting a potential hazard outside the autonomous vehicle based on the one or more occupant parameters of the occupant data, and wherein the one or more proposed driving aspects include a defensive action to increase the safety of occupants of the autonomous vehicle. Example 23. The method of Example 22,wherein the one or more occupant parameters include one or more of the following: sudden muscle tension or contraction; sudden backward movement of the occupant toward a seat back; twitching of at least one foot; speech; eye movement; pupil dilation; head movement; pulse rate; respiratory rate; and change in air intake. Example 24. The procedure according to any of Examples 22-23, wherein the defensive action to increase safety is one of the following: reducing the speed of the autonomous vehicle; signaling with hazard warning lights; tightening the seat belts; closing the windows; locking the doors; unlocking the doors; increasing the distance between the autonomous vehicle and other vehicles in the vicinity of the autonomous vehicle; alerting the authorities; altering a route; altering a stopping distance; audible signaling; and activating one or more emergency sensors,which are designed to detect potential hazards. Example 25. Method according to any of Examples 21-24, further comprising identifying correlation patterns between occupant data and driving aspects, from which the proposed driving aspects are to be identified. Example 26. Method according to any of Examples 21-25, wherein the occupant data comprises one or more of the following: history driving aspects of the occupant; context data; and occupant preference data. Example 27. Method according to any of Examples 21-26, wherein processing the occupant data comprises the following steps: detecting occupant sensation versus current driving aspects; and recording a correlation of the detected occupant sensation and the current driving aspects in an occupant profile.wherein processing the occupant data to identify one or more suggested driving aspects includes identifying the one or more suggested driving aspects based on a correlation in the occupant profile that correlates an occupant sensation and a correlated driving aspect. Example 28. The method of Example 27, wherein detecting occupant sensation includes collecting sensor data from one or more sensors that detect and monitor one or more occupant parameters, wherein processing the occupant data includes identifying occupant sensation based on the sensor data. Example 29. The method of any of Examples 21-28, wherein the suggested driving aspects include one or more of the following: a suggested speed; a suggested acceleration; a suggested cornering control; and a suggested route. Example 30. A non-perishable,A computer-readable storage medium that stores instructions which, when executed by a computing device, cause the computing device to perform the procedure according to any one of Examples 21-29. Example 31. A system comprising means for implementing the procedure according to any one of Examples 21-29. Example 32. A system for controlling an autonomous vehicle, the system comprising: an occupant monitoring system for obtaining occupant data for an occupant of the autonomous vehicle; a learning machine for processing the occupant data obtained from the occupant monitoring system to identify one or more suggested driving aspects based on the occupant data; and a vehicle interface for communicating the one or more suggested driving aspects to the autonomous vehicle. Example 33. The system according to Example 32,wherein the occupant monitoring system comprises one or more sensors for detecting one or more occupant parameters indicating an occupant response to a potential hazard outside the autonomous vehicle, wherein the learning machine processes sensor data from the one or more sensors of the occupant monitoring system to detect a potential hazard outside the autonomous vehicle based on the one or more occupant parameters, and wherein the one or more proposed driving aspects include a defensive action to increase the safety of occupants of the autonomous vehicle. Example 34. The system according to Example 33,wherein one or more occupant parameters include one or more of the following: sudden muscle tension or contraction; sudden backward movement of the occupant toward a seat back; twitching of at least one foot; speech; eye movement; pupil dilation; head movement; pulse rate; respiratory rhythm and change in air intake. Example 35. The system according to one of Examples 33-34, wherein the defensive action to increase safety is one of the following: reducing the speed of the autonomous vehicle; signaling with hazard warning lights; tightening the seat belts; closing the windows; locking the doors; unlocking the doors; increasing the distance between the autonomous vehicle and nearby vehicles; alerting the authorities; altering the route; altering the stopping distance; audible signaling; and activating one or more emergency sensors,which are designed to detect potential hazards. Example 36. The system according to any of Examples 33-35, wherein each of the one or more sensors of the occupant monitoring system monitors one occupant parameter of the one or more occupant parameters. Example 37. The system according to any of Examples 33-36, wherein the one or more sensors include one or more pressure sensors. Example 38. The system according to Example 37, wherein the one or more pressure sensors are arranged on handrails within a passenger compartment of the autonomous vehicle to detect that the occupant is tensing his or her hand muscles. Example 39. The system according to Example 37, wherein the one or more pressure sensors are arranged within a seat of the autonomous vehicle to detect occupant movement relative to the seat, including movement towards a seat backrest. Example 40. The system according to Example 37,Example 41. The system of Example 37, wherein the one or more pressure sensors are arranged on the floor of a passenger compartment of the autonomous vehicle to detect that the occupant is twitching at least one foot. Example 42. The system of any one of Examples 33-41, wherein the one or more sensors include a microphone for detecting occupant speech. Example 43. The system of any one of Examples 33-42, wherein the one or more sensors include an eye-tracker for monitoring an eye-movement parameter of the occupant.wherein the eye-tracking system comprises: a gaze direction tracker for processing occupant image data of the autonomous vehicle occupant to determine a current area of ​​the occupant's central view; and an interior view imaging system for acquiring occupant image data of the autonomous vehicle occupant for processing by the gaze direction tracker. Example 44. The system of Example 43, wherein the gaze direction tracker is designed to determine a line of sight of a current gaze direction of the autonomous vehicle occupant, to determine a field of view of the occupant based on the line of sight of the current gaze direction of the occupant, and to determine the current area of ​​the occupant's central view within the field of view. Example 45. The system of any one of Examples 33-44, wherein the one or more sensors include a pupil monitor for monitoring pupil dilation,wherein the pupil monitor comprises: a pupil tracker for processing occupant image data of a vehicle occupant to determine the size of the occupant's pupil; and an interior vision imaging system for capturing occupant image data of the vehicle occupant for processing by the pupil tracker. Example 46. The system according to any of Examples 32-45, wherein the vehicle interface of an autonomous vehicle controller communicates the one or more proposed driving aspects. Example 47. The system according to any of Examples 32-46, wherein the learning machine is to receive occupant data and identify correlation patterns between occupant data and driving aspects, and record the correlation patterns in a memory to identify the proposed driving aspects. Example 48. The system according to Example 47,where the occupant data includes prior driving aspects of the occupant's driving. Example 49. The system according to any of Examples 47-48, wherein the occupant data includes contextual data. Example 50. The system according to Example 49, wherein the contextual data includes one or more of the following: occupant age; occupant health / medical information; occupant mood; and occupant schedule information. Example 51. The system according to any of Examples 47-50, wherein the occupant data includes occupant preference data. Example 52. The system according to any of Examples 47-51, wherein the occupant monitoring system includes a statistical system designed to collect statistical data for a given geographic sector, wherein the occupant data includes statistical data. Example 53. The system according to Example 52,wherein the statistical system collects statistical data by establishing a wireless connection with a wireless network access point within the geographic sector. Example 54. The system according to any of Examples 32-53, wherein the learning machine comprises: a sensation analyzer for processing the occupant data and for detecting occupant sensation in relation to current driving aspects, wherein the sensation analyzer records a correlation of the detected occupant sensation and the current driving aspects; and an occupant profiler for maintaining an occupant profile that includes recorded correlations of an occupant sensation and a driving aspect for the occupant, wherein the learning machine identifies the one or more suggested driving aspects based on a correlation in the occupant profile of an occupant sensation and a correlated driving aspect. Example 55. The system according to Example 54,wherein the occupant monitoring system comprises one or more sensors for detecting and monitoring one or more occupant parameters, wherein the sensation analyzer detects the occupant sensation based on the sensor data from the occupant monitoring system. Example 56. The system of Example 55, wherein the one or more sensors comprise a microphone for recording occupant speech, wherein the sensation analyzer detects the occupant sensation based on the occupant speech. Example 57. The system of Example 56, wherein the sensation analyzer detects the occupant sensation using acoustic models to identify sensation via the tone of a voice. Example 58. The system of Example 56, wherein the sensation analyzer detects the occupant sensation based on speech-to-text analysis. Example 59. The system of Example 55,wherein the one or more sensors comprise biometric sensors for acquiring biometric data for one or more biometrics of the occupant, wherein the learning machine detects the occupant sensation using the biometric data. Example 60. The system of Example 59, wherein the one or more biometrics of the occupant comprise one or more of the following: occupant pulse rate; occupant blood pressure; and occupant temperature. Example 61. The system of any of Examples 55-60, wherein the one or more sensors comprise image sensors for acquiring image data of the occupant, wherein the learning machine detects the occupant sensation using the image data of the occupant. Example 62. The system of Example 54, wherein the sensation analyzer comprises a feedback system for providing a means for the occupant to express preferences.wherein the feedback system is designed to process commands from the occupant to receive preferences expressed by the occupant and to detect the occupant's sensation based on the expressed preferences. Example 63. The system of Example 62, wherein the feedback system is designed to process voice commands. Example 64. The system of Example 62, wherein the feedback system is designed to process commands supplied via a graphical user interface. Example 65. The system of Example 54, wherein the suggested driving aspects include one or more of the following: a suggested speed; a suggested acceleration; a suggested cornering control; and a suggested route. Example 66. A safety procedure in an autonomous vehicle, wherein the procedure includes: receiving sensor data from one or more sensors of an occupant monitoring system,that monitors one or more occupant parameters of an occupant of the autonomous vehicle; detects a potential hazard outside the autonomous vehicle based on the one or more occupant parameters; and communicates the detection of the potential hazard to a controller of the autonomous vehicle via a vehicle interface. Example 67. The method of Example 66, wherein communicating the detection of a potential hazard to the autonomous vehicle includes providing suggested driving aspects, including a defensive action, to increase the safety of the occupant of the autonomous vehicle. Example 68. The method of Example 67,where the defensive action to increase safety is one of the following: reducing the speed of the autonomous vehicle; signaling with hazard warning lights; tightening the seat belts; closing the windows; locking the doors; unlocking the doors; increasing the distance between the autonomous vehicle and other vehicles in the vicinity of the autonomous vehicle; alerting the authorities; altering a route; altering a stopping distance; sounding an audible signal; and activating one or more emergency sensors designed to detect potential hazards. Example 69. A non-perishable, computer-readable storage medium that stores instructions which, when executed by a computing device, cause the computing device to carry out the procedure according to any one of Examples 66-68. Example 70. A system,the means for implementing the method according to one of Examples 66-68. Example 71. A system for suggesting driving aspects of an autonomous vehicle, the system comprising: an occupant monitoring system for monitoring an occupant of the autonomous vehicle, the occupant monitoring system comprising one or more sensors for monitoring one or more occupant parameters; a detection module for processing sensor data received from the occupant monitoring system and for detecting occupant sensation relating to driving aspects of the driving performed by the autonomous vehicle, the detection module detecting the occupant sensation based on the one or more occupant parameters; a learning machine for receiving detected occupant sensation and driving aspects and for determining correlations between occupant sensation and driving aspects; an occupant profiler for maintaining an occupant profile,which includes correlations between occupant sensations and driving aspects of driving performed in the autonomous vehicle; and a vehicle interface for communicating suggested driving aspects to the autonomous vehicle, based on a comparison of a currently detected occupant sensation and an occupant sensation in the occupant profile. Example 72. The system of Example 71, wherein the one or more sensors include one or more pressure sensors. Example 73. The system of Example 72, wherein the one or more pressure sensors are arranged on handrails within a passenger compartment of the autonomous vehicle to detect that the occupant is tensing his or her hand muscles. Example 74. The system of Example 72, wherein the one or more pressure sensors are arranged within a seat of the autonomous vehicle to detect occupant movement relative to the seat.including a movement towards a seat backrest. Example 75. The system of Example 72, wherein the one or more pressure sensors are arranged on a floor of a passenger compartment of the autonomous vehicle to detect that the occupant is twitching at least one foot. Example 76. The system of Example 72, wherein the one or more pressure sensors are arranged within a seat of the autonomous vehicle to detect a breathing rhythm. Example 77. The system of any one of Examples 71-76, wherein the one or more sensors include a microphone for detecting occupant speech. Example 78. The system of any one of Examples 71-77, wherein the occupant monitoring system includes a statistical system designed to collect statistical data for a given geographical sector, the detection module processing the statistical data. Example 79. The system of Example 78,wherein the statistical system collects statistical data by establishing a wireless connection with a wireless network access point within the geographic sector. Example 80. The system according to any of Examples 71-79, wherein the learning machine comprises: a sensation analyzer for processing the occupant data and for detecting occupant sensation in relation to current driving aspects, wherein the sensation analyzer records a correlation between the detected occupant sensation and the current driving aspects; and an occupant profiler for maintaining an occupant profile that includes recorded correlations of occupant sensations and driving aspects for the occupant, wherein the learning machine identifies the one or more suggested driving aspects based on a correlation in the occupant profile of an occupant sensation and a correlated driving aspect. Example 81. An autonomous vehicle,which includes the following: an occupant monitoring system for monitoring an occupant of the autonomous vehicle, wherein the occupant monitoring system comprises one or more sensors for monitoring one or more occupant parameters; a detection module for processing sensor data received from the one or more sensors of the occupant monitoring system and for detecting a potential threat outside the autonomous vehicle based on the one or more occupant parameters; and an autonomous vehicle control system for determining and causing the autonomous vehicle to perform a defensive action based on the detected potential threat. Example 82. Autonomous vehicle comprising: an occupant monitoring system for receiving occupant data for an occupant of the autonomous vehicle; a machine learning system for processing occupant data received from the occupant monitoring system,to identify one or more suggested driving aspects based on occupant data; and an autonomous vehicle controller for providing autonomous navigation and controlling the autonomous vehicle, wherein the autonomous vehicle controller receives the one or more suggested driving aspects and causes the autonomous vehicle to execute at least one of the one or more suggested driving aspects. Example 83. The autonomous vehicle according to Example 82, wherein the occupant monitoring system comprises one or more sensors for detecting one or more occupant parameters indicating an occupant response to a potential hazard outside the autonomous vehicle, wherein the learning machine processes sensor data from the one or more sensors of the occupant monitoring system to detect a potential hazard outside the autonomous vehicle based on the one or more occupant parameters,and wherein one or more driving aspects include a defensive action to increase the safety of occupants of the autonomous vehicle. Example 84. The autonomous vehicle according to any of Examples 82-83, wherein the learning machine comprises: a sensation analyzer for processing occupant data and detecting occupant sensation in relation to current driving aspects, wherein the sensation analyzer records a correlation between the detected occupant sensation and the current driving aspects; and an occupant profiler for maintaining an occupant profile that includes recorded correlations of occupant sensations and driving aspects for the occupant, wherein the learning machine identifies the one or more suggested driving aspects based on a correlation in the occupant profile of an occupant sensation and a correlated driving aspect. Example 85. The autonomous vehicle according to Example 84,wherein the occupant monitoring system comprises a detection module with one or more sensors for detecting and monitoring one or more occupant parameters, wherein the sensation analyzer detects the occupant sensation based on the sensor data from the occupant monitoring system.

Claims

Device for use with a vehicle (100), wherein the device (102) comprises: at least one sensor (112) for measuring a first biometric of a first occupant of the vehicle (100) and a second biometric of a second occupant of the vehicle (100); at least one memory (228); and a processor (226) for executing instructions for: determining a first occupant emotion of the first occupant based on the first biometric, determining a second occupant emotion of the second occupant based on the second biometric, and providing a suggestion within the vehicle (100) based on the first occupant emotion and the second occupant emotion, wherein the at least one sensor (112) is positioned on a door handle of the vehicle (100). Device according to claim 1, wherein the at least one sensor (112) includes an image sensor (112b), wherein the image sensor (112b) is to detect a facial expression, and wherein the determination of the first occupant emotion and / or the second occupant emotion is at least partially based on the facial expression. Device according to claim 1, wherein the first and / or the second biometrics include a pulse rate. Device according to claim 1, wherein the at least one sensor (112) includes a microphone (112a). Device according to claim 1, wherein the at least one sensor (112) includes a pressure sensor (112g) which is to be grasped by the first occupant and / or the second occupant. Device according to claim 1, wherein the at least one sensor (112) is to measure pupil dilation. A method comprising: measuring, via at least one sensor (112), the first biometrics of a first occupant of a vehicle (100) and the second biometrics of a second occupant of the vehicle (100); determining, by executing instructions with at least one processor, the first occupant emotion of the first occupant based on the first biometrics; determining, by executing instructions with the at least one processor, the second occupant emotion of the second occupant based on the second biometrics; delivering, by executing instructions with the at least one processor, a suggestion within the vehicle (100) based on the first occupant emotion and the second occupant emotion, wherein the measurement of the first biometrics and the second biometrics involves utilizing the at least one sensor (112) positioned on a door handle of the vehicle (100). The method of claim 7, wherein the measurement of the first biometrics and the second biometrics includes measuring, via an image sensor (112b), at least one facial expression, and wherein the determination of the first occupant emotion and / or the determination of the second occupant emotion is at least partially based on the at least one facial expression. Method according to claim 7, wherein the measurement of the first biometrics and the second biometrics includes measuring at least one pulse rate. Method according to claim 7, wherein the measurement of the first biometrics and the second biometrics is carried out via at least one microphone (112a). Method according to claim 7, wherein the at least one sensor (112) includes a pressure sensor (112g) which is to be grasped by the first occupant and the second occupant. Method according to claim 7, wherein the measurement of the first biometrics and the second biometrics includes measuring at least one pupil dilation. A computer-readable medium comprising instructions which, when executed, cause at least one processor (226) to perform a method according to claims 7 to 12. Equipment comprising means for carrying out a method according to claims 7 to 12. Vehicle comprising: at least one sensor (112) for measuring a first biometric of a first occupant of the vehicle (100) and a second biometric of a second occupant of the vehicle (100); at least one memory (228); and a processor (226) for executing instructions for: determining a first occupant emotion of the first occupant based on the first biometric, determining a second occupant emotion of the second occupant based on the second biometric, and providing a suggestion within the vehicle (100) based on the first occupant emotion and the second occupant emotion, wherein the at least one sensor (112) is positioned on a door handle of the vehicle (100). Vehicle according to claim 15, wherein the at least one sensor (112) includes an image sensor (112b), wherein the image sensor (112b) is intended to detect a facial expression, and wherein the determination of the first occupant emotion and / or the second occupant emotion is at least partially based on the facial expression. Vehicle according to claim 15, wherein the first and / or the second biometrics include a pulse rate. Vehicle according to claim 15, wherein the at least one sensor (112) includes a microphone (112a). Vehicle according to claim 15, wherein the at least one sensor (112) includes a pressure sensor (112g) which is to be grasped by the first occupant and / or the second occupant. Vehicle according to claim 15, wherein the at least one sensor (112) is to measure pupil dilation.

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