601 results about "Road user" patented technology

A system and method for facilitating collection of Road User Charges (RUC) using a digital currency within a Distributed Ledger Technology (DLT) network are disclosed. The system generates secure digital wallets for Road Users and Facility Owners, receives trip, position, distance, and cost data from vehicles, sensors, and facility systems, and encodes settlement logic into smart contracts. These contracts automatically execute jurisdiction-specific disbursements across blockchain, directed acyclic graph (DAG), or hashgraph frameworks, ensuring scalability and auditability without reliance on centralized tolling infrastructure. An AI Activity Module processes multi-source telemetry—including vehicle sensors, GNSS / PNT data, roadside infrastructure, and third-party traffic feeds—to detect congestion, forecast conditions, optimize routing, and dynamically adjust segment-level RUC pricing in real time. The integration of AI-driven adaptive pricing with distributed ledger settlement provides a decentralized, infrastructure-agnostic framework for secure, transparent, and verifiable road use fee collection across multiple jurisdictions, distinct from conventional tolling systems.

A Level IV fuel-saving robot system for heavy-duty trucks (HDT) focuses on the minimization of actual fuel consumption (L / 100 km) for long-haul freight based on an electrical power split device and a mixed hybrid powertrain architecture. The Level IV fuel-saving robot has an L4 autonomous driving function within the Operational Design Domain (ODD) of expressways, operates in a “shadow mode” or “Disengagement Mode,” automatically generates a discrepancy report or detachment report, completes the “3R” (Real Vehicle, Real Road, Real Payload) batch validation for an L4 system on a billion mile scale quickly with high performance to cost ratio under the condition of ensuring the traffic safety of existing road users and reduces the total validation expense by more than 65% compared with the modern HDT with internal combustion engine equipped with the L4 system, promoting the early commercialization of the fuel-saving robot.

The invention discloses a training method and device of an automatic driving decision model, a medium and a product, and relates to the technical field of automatic driving, iteratively training the decision model by adopting a trained world model, and inputting a driving action output by the decision model and a traffic structure condition corresponding to the driving action into the trained world model. The award value of the driving action is calculated according to the obtained future driving scene information, and the decision model is updated according to the award value, so that efficient closed-loop optimization of the decision model is realized, the perception ability and decision ability of the model can be remarkably improved, and the training efficiency is improved. The first reward value based on the road and the second reward value based on the road user are constructed through the future driving scene, the advantages and disadvantages of the driving action are evaluated from the two aspects of the road and the road user, the driving action is optimized, the safety and reliability of the automatic driving decision are improved, and the automatic driving performance of the vehicle in the complex traffic environment is improved.

Methods and apparatus for extracting headway information from LiDAR trajectory data received from a roadside LiDAR sensor system. In an embodiment, a LiDAR data processor pre-processes raw roadside LiDAR sensor data, extracts headway data from the pre-processed raw roadside LiDAR sensor data and extracts critical headway data from the extracted headway data. In some implementations, the pre-processing may include filtering background information from the raw roadside LiDAR sensor data, clustering objects in the filtered LiDAR sensor data, and classifying the clustered objects, wherein the clustered objects represent road users. In addition, in some embodiments the LiDAR data processor may track movement of the clustered objects in real-time to extract trajectory and speed data, and then map the extracted trajectory and speed data such that the clustered objects are georeferenced.

A system and method for controlling a road user assistance network includes obtaining historical data, obtaining geo-fence data, generating probabilistic states for a future time, determining spatial proximity data for a feature based on the geo-fence data, generating a prediction based on the probabilistic state and the spatial proximity data, communicating the prediction to a road user or a roadside device and controlling the road user or roadside device based on the prediction.

The disclosure relates to a method (100) for coordinating a first road user (10) with a second road user (20) located in an environment of the first road user (10). Furthermore, the disclosure relates to a system (40) comprising at least two motorized vehicles (50, 60), wherein the system (40) is designed to carry out a method according to one of the preceding method claims, wherein at least one of the motorized vehicles (50) is designed to carry out the method steps in order to act as the first road user (10), and wherein at least one of the motorized vehicles (60) is designed to carry out the method steps in order to act as the second road user (20).

The invention relates to a method for providing, by means of a warning device (12), an adaptation notification, preferably a warning notification, to at least one ego road user (10) to be notified to adapt at least one driving function to at least one surroundings road user (32) located and / or moving in the surrounding region (2) of the road user, said warning device being provided with respective movement paths (PCL, PR) which are predicted for the ego road user and for the at least one surroundings road user (32) and along which the movements of the road users (10, 32) are expected. The warning device (12) determines, on the basis of the predicted movement paths (PCL, PR), at least one notification variable, which is characteristic of a provision of the adaptation notification and which is preferably characteristic of whether an adaptation notification (I1, A32) is to be provided and / or at what time the adaptation notification (I1, A32) is to be provided. According to the invention, the warning device (12) determines the notification variable on the basis of at least one swarm prediction variable, in particular at least one statistical swarm prediction variable, for determining the prediction quality of at least one of the predicted movement paths (PCL, PR), wherein the at least one swarm prediction variable is determined on the basis of global swarm movement data of a plurality of road users, said movement data being generated in a plurality of different traffic regions (2).

A warning system (100) for warning a vehicle driver; the system having a sensor (3a, 3b) for capturing ambient information; a processing unit (4) for processing information captured by the sensor; and a output unit (5, 7, 8, 9) for outputting a warning signal to the vehicle driver (1); wherein the sensor (3a, 3b) is adapted to detect information about other road users; the processing unit (4) is adapted to analyze the information with respect to the presence of one or more certain characteristics of the other road users, and to output a signal to the output unit (5) for outputting the warning signal when the analysis detects the presence of certain characteristics of other road users.

A method for predicting trajectories of road users includes (i) representing a traffic scene as an agent interaction graph, each having a node for a road user corresponding to a target vehicle and for one or more other road users and having a plurality of edges, wherein each edge between two of the nodes is associated with a respective edge type, which indicates a type of movement of the road users represented by the nodes relative to each other on a respective roadway, (ii) processing the agent interaction graph by a graph transformer to determine embeddings of the target vehicle and the one or more other road users, wherein the graph transformer has an attention mechanism which takes into account the edge types of the edges of the agent interaction graph, and (iii) predicting at least one trajectory of the target vehicle from the embeddings.

The technology involves pick-up and drop-off techniques for autonomous vehicles. Pullover selection options are evaluated by considering road user inconvenience and dynamic changes to such inconvenience. In response, a change in vehicle operation is made as needed. This process may consider whether a given situation is “likely-inconvenient” or “unlikely-inconvenient” based on both statie and dynamic signals from the environment for a likely pick-up or drop-off location. The vehicle may request remote support to determine whether to pull over or to move from a stopped location, for instance based on changes to dynamic signals associated with other road users. In double parking situations, the vehicle may determine whether to remain double parked or move to limit inconvenience to a minimum number of other vehicles, such as by blocking as few other vehicles as possible or by making it as easy as possible for a parked vehicle to pull out.

This invention provides a bidirectional interactive traffic-control management system with improved operational architecture through edge computation. The system comprises a server including a road and traffic network information subsystem, and an urban traffic control subsystem; and a plurality of terminal devices. For the purpose of edge computing, the plurality of terminal devices, located respectively at a road user end or a roadway end, is independently provided from a central server. The urban traffic control subsystem further includes an intersection grouping module for grouping adjacent intersections according to a correlation nature of their approach arrival-flow patterns, wherein the intersections in the digital urban traffic-control road network are divided into signal groups including isolated intersection groups, arterial intersection groups, and network intersection groups according to the correlation nature of their approach arrival-flow patterns, either high or low, between adjacent intersections in the urban traffic control network.

A method for classifying a driving behavior of a road user in the environment of an ego vehicle. The method includes: receiving trajectory data of a driving trajectory of a road user arranged in the environment of an ego vehicle; ascertaining a latent-space representation of the driving trajectory of the road user in a latent space; ascertaining a distance of the latent-space representation of the driving trajectory of the road user to a latent-space representation of at least one normal trajectory in the latent space and classifying a driving behavior of the road user as a normal driving behavior if the distance in the latent space falls below a predetermined limit value; or classifying the driving behavior of the road user as an abnormal driving behavior if the distance in the latent space exceeds the predetermined limit value.

To provide a vehicle collision detection device capable of accurately determining collisions with vulnerable road users.SOLUTION: A processor 152 of an ECU 150 includes: an object probability calculation unit 152b that calculates a first probability indicating the likelihood that an object in front of a vehicle is a pedestrian or a bicycle; a collision probability calculation unit 152c that calculates a second probability indicating the probability of the vehicle colliding with the object; a collision prediction probability calculation unit 152a that calculates a collision prediction probability indicating the probability of colliding with the pedestrian or the bicycle based on the first probability and the second probability; a collision determination threshold acquisition unit 152d that acquires a collision determination threshold for the vehicle's deceleration based on the collision prediction probability; and a collision determination unit 152e that determines whether the vehicle collided with the pedestrian or the bicycle based on the detected value of the vehicle's deceleration and the collision determination threshold.SELECTED DRAWING: Figure 2

The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a “continuing” lane driving operation, such as going straight in a turn-only lane. Sensor data from detected / observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.

A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and / or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and / or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

Disclosed embodiments include technologies for improving safety mechanisms in computer assisted and / or automated driving (CA / AD) vehicles for protecting vulnerable road users (VRUs). Embodiments include various mechanisms to enable early Responsibility Sensitive Safety (RSS) checks for the CA / AD vehicles driving policy to protect in-danger VRUs. Embodiments also include controlled forwarding mechanisms to notify other CA / AD vehicles and roadside infrastructure when a potentially dangerous situation is detected. Other embodiments are described and / or claimed.

The invention relates to a crash protection device for a roof frame of a motor vehicle, comprising at least the following components: - a protective element for absorbing a collision load; - a joint arrangement for moving the protective element between a normal position and a protective position; and - a drive unit for actuating the joint arrangement to transfer the protective element into the protective position, wherein, when viewed in use from a design collision direction, the associated roof frame is covered by the protective element. The crash protection device proposed here represents an efficient and cost-effective measure for the protection of road users.

The invention provides a general highway risk identification and early warning system based on global linkage, and belongs to the technical field of traffic safety, the general highway risk identification and early warning system specifically comprises a monitoring module, an independent power supply module and an early warning broadcast module, the monitoring module is used for monitoring and identifying geological disaster risks based on various sensors and generating early warning information, and the independent power supply module is used for sending the early warning information to the early warning broadcast module; the early warning broadcast module is used for transmitting geological disaster risks identified by the monitoring module to a road user or a management department through acousto-optic warning and wireless communication methods according to early warning information, and the independent power supply module is used for providing energy support for the monitoring module and the early warning broadcast module. And the equipment can normally operate in remote mountainous areas and environments with insufficient power grid coverage. According to the scheme, the risk disposal speed and accuracy are improved; the manual inspection cost is reduced, and the coverage range and the real-time performance of monitoring are improved; the false alarm rate is effectively reduced through multi-sensor data fusion; the system adapts to various extreme environments and ensures stable operation of the system.

The invention provides an intelligent vehicle lamp self-adaptive adjusting system which comprises a multi-mode information collecting module, a driver body shape analyzing module, a vehicle lamp adjusting module and an electric executing mechanism, and the vehicle lamp adjusting module is used for generating vehicle lamp adjusting parameters according to key body shape parameters of a driver, vehicle dynamic state information and natural environment information; wherein the vehicle lamp adjusting parameters at least comprise vehicle lamp height, light beam angle and light beam width adjusting parameters. By means of the mode, self-adaptive adjustment of the height, the angle and the light beam width of the vehicle lamp is achieved by combining the physical characteristics, the dynamic behaviors, the vehicle dynamic state and the natural environment information of the driver, the view area of the driver is accurately covered, and the driving safety and comfort are remarkably improved; the real-time response capability and the closed-loop optimization mechanism of the system enable the system to adapt to complex and changeable driving scenes, and meanwhile, interference to other road users is reduced.

A method that is computer implemented for self-learning of relevancy metrics for perception related applications, the method comprising: receiving a training dataset that comprises (i) scenario information regarding a scenario faced by a vehicle and involving a road user, and (ii) behavior information indicative of a response of the vehicle to a presence of the road user; determining, based on the scenario information and the behavior information, a relevancy metric providing an indication of an impact of the road user on a driving of the vehicle; and training, using self-learning, a machine learning process to infer the relevancy metric according to the scenario.

A computer-implemented method for warning road users of a hazardous operating condition of a heavy-duty vehicle. The method comprises obtaining, by a processor device of a computer system, a speed limit interval associated with a road on which a heavy-duty vehicle is travelling, and wherein the speed limit interval is based on a speed limit of the road; determining, by the processor device, that a travelling speed of the heavy-duty vehicle is outside the speed limit interval; determining, by the processor device, that a travelling angle of the heavy-duty vehicle and / or of the road on which the heavy-duty vehicle is travelling is outside a predetermined travelling angle range; and automatically controlling, by the processor device, the vehicle hazard lights of the heavy-duty vehicle based on the travelling speed and the travelling angle.

The invention provides a vulnerable road user trajectory prediction method and system based on motion query enhancement, and the method comprises the steps: carrying out the alignment of the current position and direction of each motion sample through a normalization process, carrying out the clustering of the end points of the normalized motion samples, taking an obtained clustering center as a generated static query, and carrying out the prediction of the trajectory of a vulnerable road user. Using extended static queries (DSQ) to extend the coverage of the static queries to provide more diverse motion patterns; extracting and fusing track features and map features to obtain map features and enhanced track features for subsequently obtaining updated query features of each layer, establishing connection between dynamic queries output by a shallow decoder and a deep decoder by using cross-linked dynamic query (BDQ), and coordinating query optimization between decoding layers; and performing prediction result integration on the plurality of homogeneous models to obtain a final prediction trajectory. According to the method, the characteristics capable of reflecting the motion trend of the vulnerable road user can be more effectively extracted, so that the prediction result is more accurate.

A driving assistance system, a driving assistance method, a non-transitory computer-readable storage medium storing a driving assistance program for assisting driving of a host vehicle plans a behavior change that is a change in a driving behavior controlled in the host vehicle, and projects a notification image that provides notification of a transition state of the behavior change onto a traveling road to cause a different road user to recognize the notification image, the different road user being expected to interact with the host vehicle.

The invention discloses a two-way multi-lane highway traffic flow dynamic control method and system based on traffic simulation, and the method comprises the steps: firstly constructing a virtual sensor network covering two-way multiple lanes through a traffic simulation platform, carrying out the total factor perception of a traffic flow, and obtaining the collection data; secondly, abnormal values in the collected data are filtered in a mode of combining a double-sliding-window mechanism and an IQR algorithm, and filtered data are obtained; according to the invention, a function of performing real-time dynamic control on the expressway traffic flow by constructing a closed-loop optimization system of perception, decision, control and evaluation is realized, and accurate perception, intelligent decision and cooperative control can be performed on the traffic flow by fusing dynamic traffic simulation and a real-time data driving technology. Meanwhile, the traffic flow evolution trend pre-judgment capability can be improved through a time-space coupled traffic state prediction model, the overall traffic capacity of the expressway is improved, the life and property safety of road users is guaranteed, and the method is suitable for being widely popularized and used.

Systems and methods for externally communicating vehicular gaming status are disclosed herein. One embodiment of a gaming-status communication system detects automatically, in a connected vehicle, activation of a gaming mode of the connected vehicle in which a gaming system of the connected vehicle supports at least one of (1) electronic game playing by one or more occupants of the connected vehicle and (2) multi-vehicle networked electronic gaming involving the connected vehicle and one or more other connected vehicles. In response to detecting the activation of the gaming mode, the system activates automatically one or more external human-machine interface (HMI) indicators on the exterior of the connected vehicle to communicate, to other road users, information pertaining to the gaming mode of the connected vehicle.

This application describes a proactive sensing system for an autonomous vehicle. This system fuses vehicle sensing data and sensing data from a roadside unit, a Traffic Control Unit, and / or a cloud to provide full 360-degree coverage and birds-eye view of the driving environment. This proactive sensing system cooperatively uses vehicle-based sensor data and sensor data from external sources to provide better and more efficient sensing of longtail or corner cases, such as blind spots and blockage by surrounding objects. Specifically, this proactive sensing system effectively identifies major sensing points where vulnerable road users, such as pedestrians and bicycles, are major challenges for autonomous vehicles at intersections, roundabouts, or work zones. Accordingly, the technology significantly improves the safety of autonomous vehicles.

A method for providing information detected by on-board sensors about road users in the surroundings of a vehicle involves providing the detected information as data structures representing vectors. Every vector is thereby associated with a cell of a predefined vehicle-mounted occupancy grid, in which a road user described by the respective information is situated. Every vector at least includes coordinates of an associated cell, in which the respective road user is situated, a speed vector, which represents a speed of the respective road user, a time stamp, which represents a point in time of a detection of the respective road user, and an object class, which represents a type of the respective road user.

A method for assisting an automated vehicle in vehicle control, the method including continuously checking whether the automated vehicle is leaving its operational design domain and whether a comparison metric of the deviation of current environmental conditions from the associated information on a digital map is exceeded. If so, the method further includes requesting an authorization to enter a vehicle-following mode at a control center; and rejecting the request or granting the request by the control center, wherein when the request is granted by the control center, a start of the vehicle-following mode and the road user to be followed are determined. The method additionally includes carrying out a vehicle-following mode by the vehicle at the start determined by the control center behind the road user determined by the control center.

The invention relates to the technical field of automobile accessories, in particular to a self-luminous safety windscreen wiper and a using method thereof.The self-luminous safety windscreen wiper comprises a steel sheet and a windscreen wiper strip installed at the bottom of the steel sheet; a base is installed on the outer wall of the steel sheet, a protruding base is fixed to the top of the base, and a windscreen wiper connecting piece is installed outside the protruding base. The windscreen wiper strip comprises a steel sheet, a mounting strip is mounted outside the steel sheet, an embedding groove is formed in the outer wall of the mounting strip, and a light-emitting strip is mounted in the embedding groove. Therefore, other road users (pedestrians and other vehicle drivers) can timely find and recognize the opposite vehicle in extreme weather, the risk of traffic accidents is reduced or even avoided, an additional warning function can be provided when the vehicle breaks down or road rescue is carried out, and the requirement for driving safety can be well met.