784 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 ACE HDTs of the present disclosure focuses on the minimization of actual fuel consumption (L / 100 km) for long-haul freight at first based on an electrical power split device (ePSD) and a mixed hybrid powertrain architecture. A basic model Level I fuel-saving robot realizes a longitudinal L1 automatic driving function through a predictive adaptive cruise (PACC) technology within an Operational Design Domain (ODD) for highways and reduces the actual fuel consumption of an ACE HDT by more than 20% compared with modern diesel HDTs, and the energy-saving and emission-reducing effect of the basic model Level I fuel-saving robot is decoupled from both the technical level of a vehicle engine and the driving level of a driver; an advanced Level IV fuel-saving robot has a L4 automatic driving function within the ODD for highways, operates in a “shadow mode” or “detached mode”, automatically generates a discrepancy report or detachment report, completes the “3R” batch validation for an L4 system on a billion mile scale quickly with high cost effectiveness on the premise 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 Level IV fuel-saving robot.

The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior.

A request signal that indicates a quality for a determination of an orientation of a road user is received. The orientation of the road user is determined based on a) image data when the request signal indicates the quality is below a predetermined quality for the determination of the orientation of the road user, or b) on LIDAR data and image data when the request signal indicates the quality is the predetermined quality.

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.

The invention relates to a method for situational hazard assessment (24) of an operating situation (16) of a vehicle (12), comprising environmental sensing (28) of a vehicle environment (30) that at least partially includes one road user (14) during the operating situation (16) of the vehicle (12) and calculation (32) of a hazard probability (34) in the operating situation (16) depending on at least one operating parameter (36) of the vehicle (12), wherein the hazard probability (34) depends on the at least one road user (14), the controllability (22) of the road user (14) in connection with the operating situation (16) is estimated, and the hazard probability (34) is calculated depending on the estimated controllability (22). Furthermore, the invention relates to a method for vehicle operation (10).

Map and kinematic based predictions are used for determining the trajectory of road users for use by a host vehicle. A kinematic trajectory of a road user is determined. The road user is associated with mapped lanes. At least one path of the road user is predicted using the mapped lanes. For a path of the at least one path, a probability is generated using the kinematic trajectory of the road user, wherein the probability represents how likely the road user will continue following the path. A kinematic prediction of the road user is generated using the probability corresponding to the path, wherein the kinematic prediction represents how likely the road user will follow an alternative path to the at least one path. Using at least one control system of a vehicle, a control action for the vehicle is determined using the at least one path and the kinematic prediction.

A warning system is provided for alerting a VRU of a predicted collision. The system includes one or more input devices for transmitting first and second input signals for associated first and second vehicles. The system further includes a UWB beacon communicating with a UWB tag of a VRU notification device carried by the VRU. A location of the UWB tag is trackable via a third input signal. The system further includes a computer having one or more processors and a non-transitory computer readable medium. The processor is programmed to determine the location of the VRU and further determine the predicted collision between the second vehicle and the VRU. The processor is further programmed to generate a notification actuation signal associated with the VRU at a tracked location. The VRU notification device notifies the VRU that the second vehicle is headed toward the predicted collision site in the crosswalk.

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 embodiment of the invention provides a data processing method and device, electronic equipment, a system and a storage medium. The method comprises the following steps: receiving acquisition data streams generated by roadside sensing equipment in a sensing area; based on the transmission time delay between each roadside sensing device and each edge cloud node in the edge cloud platform, obtaining a first edge cloud node satisfying a first transmission condition, the first transmission condition comprising the lowest transmission time delay; distributing the collected data flow to a plurality of computing power units under the first edge cloud node for data preprocessing; performing data fusion on the preprocessed data to form target fusion data; and transmitting the target fusion data to an auxiliary decision making system of the road user. Before data transmission, the node with the lowest transmission delay is selected as the target node for data processing, so that the delay caused by data transmission is reduced, preprocessing and data fusion are completed at the edge cloud nodes, multiple independent and dispersed data can be integrated, and the data volume is reduced.

A method for traffic sign prediction includes determining a plurality of roadway characteristics. The method also includes determining a plurality of road user information. The method also includes determining a plurality of weather-related information. The method also includes determining a predicted traffic sign based at least in part on the plurality of roadway characteristics, the plurality of road user information, and the plurality of weather-related information. The method also includes performing an action based at least in part on the predicted traffic sign.

Categorizing driving behaviors of other road users includes maintaining a first history of first lateral-offset values of a road user with respect to a center line of a lane of a road; determining a first pattern based on the first history of the first lateral-offset values; determining a driving behavior of the road user based on the first pattern; and autonomously performing, by a host vehicle, a driving maneuver based on the driving behavior of the road user. The first history can be maintained for a predetermined period of time. An apparatus includes a processor that is configured to track a trajectory history of a road user; determine, based on the trajectory history, a driving behavior of the road user; and transmit a notification of the driving behavior.

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).

Techniques are disclosed for reducing false positives for generating warnings to avoid potential collisions between a vehicle and vulnerable road users (VRUs). This is accomplished via an onboard vehicle safety system that uses crowd-sourced map data to determine whether a vehicle is capable of performing a maneuver that results in a lateral shift of the vehicle (which may include a lane-shifting or turning maneuver) within a predetermined threshold time period. The ability for the vehicle to make the turning maneuver, among other driving scenarios, may be used to by the safety system to intelligently determine whether a warning or other action is needed to avoid a potential collision with a VRU. In this way, the occurrence and number of false warnings / interventions are minimized or at least reduced, leading to more attentive drivers and thereby improving VRU safety.

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.

The present invention relates to a method for collecting data for subsequent training of an object detection model of an automated driving system. The method includes, in response to detecting an evasive maneuver of the vehicle or a further road user, obtaining sensor data pertaining to a scene at which the evasive maneuver was detected; determining by the object detection model, whether an object is detected in the scene based on the sensor data; and in response to no object being detected, storing the sensor data for subsequent training of the object detection model. The present invention further relates to a method performed in a server.

A method controls an at least partially autonomous ego motor vehicle having a positive driving behavior in the event of turning off or a lane change of a relevant road user. The method includes determining a current shortest distance between the ego motor vehicle and the relevant road user, determining a safety measure which contains a collision probability, determining an ideal speed of the ego motor vehicle at which a predefined safety measure is attainable, and determining a speed-based adjustment value for acceleration and / or deceleration of the ego motor vehicle based on the ideal speed. The method further includes determining a distance-based adjustment value for acceleration and / or deceleration of the ego motor vehicle based on a hazard factor kept available by the storage unit, selecting between the speed-based adjustment value and the distance-based adjustment value, and controlling deceleration or acceleration of the ego motor vehicle.

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.

The present invention relates to a method for operating an assistance system of an ego-vehicle 1, in particular a lane guidance assistant or a system for automated vehicle control in a curve area, wherein existing curve curvatures and road users 3 are detected in the curve area, wherein, in the course of detecting the road users 3, their vehicle speed, direction of travel and road user type are determined, wherein a movement corridor B for the ego-vehicle 1 is determined and a reaction measure is controlled depending on a predicted collision risk of the ego-vehicle 1 with an oncoming road user 3.

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.

A processing method, which is executed by a processor for performing a processing related to a driving of a host moving object, includes a performance achievement prediction that predicts a future action of an other road user in an external environment of the host moving object as a prediction for achieving a target performance of the host moving object; a driving plan that plans the driving of the host moving object according to the performance achievement prediction; a safety assurance prediction that predicts, independently of the performance achievement prediction, the future action of the other road user in the external environment as a prediction for assuring a reasonably foreseeable safety of the host moving object; and a driving monitoring that monitors the driving of the host moving object according to the safety assurance prediction.

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 method relates to a computer-implemented method (100) for generating a knowledge diagram (400) for traffic movement prediction, comprising: Receiving (101) environmental sensor data (503) from at least one environmental sensor (505) of an ego vehicle (501); Receiving (103) map data (509) from an electronic road map (511); Extracting (105) information about the at least one road user (507) from the environmental sensor data (503) and extracting information about the movement lane (515) on which the road user (507) is located from the map data (509);and generating (107) a knowledge diagram (400) of the road network (513) in the vicinity of the ego-vehicle (501), comprising nodes (401) and edges (403), based on the map data (509) and / or the environmental sensor data (503), wherein the knowledge diagram (400) comprises at least one node (401) representing the road user (507) and at least one node (401) representing the lane on which the road user (507) is located. The invention further relates to a method (200) for predicting traffic movement. The invention further relates to a method (300) for controlling an ego-vehicle (501);

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.

The invention discloses a crossing intention judgment method, a medium, equipment and a vehicle. The crossing intention judgment method comprises the following steps: constructing a data set by using a vulnerable road user crossing positive example meeting a first preset condition and a vulnerable road user crossing negative example meeting a second preset condition; wherein the first preset condition is that the vulnerable road user has a passing behavior in front of the vehicle in the region of interest of the vehicle in the future preset time; the second preset condition is that the vulnerable road user is in the region of interest of the vehicle and has a passing behavior behind the vehicle in the future preset time; training a preset dichotomy model by adopting the data set, wherein the dichotomy model is built based on a deep learning model; and deploying the trained dichotomy model to the automatic driving system so as to judge the crossing intention of the vulnerable road user. Through the above mode, the judgment accuracy of the crossing intention of the vulnerable road user can be improved, and then the safety and comfort of passengers are improved.