A vehicle locating method, an apparatus, an electronic device, a storage medium and a computer program product are provided, and relate to the technical field of intelligent transportation. The method includes: acquiring an image of a current road within an area in which a target vehicle is currently located, and acquiring current lane related information within the area in which the target vehicle is currently located from a map application; 5determining a current road recognition result based on the image of the current road; and determining a lane in which the target vehicle is currently located based on at least one of the current road recognition result and the current lane related information, and taking the lane in which the target vehicle is currently located as the current lane location result for the target vehicle.

A system provides to a vehicle approaching an intersection information about objects of interest that are in a vicinity of the intersection. The system includes a remote server system that receives, from each node of a network of nodes at various intersections, augmented perception data (APD) representing a set of objects of interest that are proximate the intersection the node is located. The APD is extracted from images captured by a node vision system. The remote server system receives a query from the vehicle for APD associated with an imminent path of a planned route of the vehicle. The remote server system searches for resultant APD associated with the path and communicates the resultant APD to the vehicle. The objects of interest associated with an imminent intersection and objects of interest captured by the vehicle are fused together to control navigation of the vehicle through the imminent intersection.

Provided are methods for light-based object localization, which can include comparing unexpected light sources to expected light sources for determination of an agent, such as a partially and/or fully occluded agent. Some methods described also include generating a trajectory for an autonomous vehicle based on the comparison. Systems and computer program products are also provided.

Examples disclosed herein may involve a computing system that is operable to (i) receive a first sequence of images captured by a monocular camera associated with a vehicle during a given period of operation and a second sequence of image pairs captured by a stereo camera associated with the vehicle during the given period of operation, (ii) derive, from the first sequence of images captured by the monocular camera, a first track for a given agent that comprises a first sequence of position information for the given agent, (iii) derive, from the second sequence of image pairs captured by the stereo camera, a second track for the given agent that comprises a second sequence of position information for the given agent, and (iv) determine a trajectory for the given agent based on the first and second tracks for the given agent.

Systems and methods are provided for controlling vehicle operation. A processor may access route information for navigation of a route by the vehicle including data relating to speed along the route and calculate a speed of the vehicle along the route based on the route information. The processor may cause the vehicle to be operated at the calculated speed along the route; obtain dynamic information for the route based on data collected from one or more other vehicles on the route and indicating current conditions on the route which affect the speed of the vehicle along the route; and cause the vehicle to be operated at an updated speed along the route, based on the dynamic information.

The invention is notably directed to a method of steering an automated vehicle (2) in a designated area, thanks to a set (10) of offboard perception sensors (110-140). The method comprises repeatedly executing algorithmic iterations, where each iteration comprises the following steps. First, sensor data are dispatched to K processing systems (11, 12), whereby each processing system k of the K processing systems receives N.sub.k datasets of the sensor data as obtained from N.sub.k respective sensors of the set (10) of offboard perception sensors (110-140), where k=1 to K, K2, and N.sub.k2. The N.sub.k datasets are subsequently processed at each processing system k to obtain M.sub.k occupancy grids corresponding to perceptions from M.sub.k respective sensors of the offboard perception sensors, respectively, where N.sub.kM.sub.k1. The M.sub.k occupancy grids overlap at least partly. Data from the M.sub.k occupancy grids obtained are then fused, at each processing system k, to form a fused occupancy grid, whereby K fused occupancy grids are formed by the K processing systems (11, 12), respectively. The K fused occupancy grids are then forwarded to a further processing system (14), which merges the K fused occupancy grids to obtain a global occupancy grid for the designated area. Eventually, a trajectory is determined for the automated vehicle (2), based on the global occupancy grid. This trajectory is then forwarded to a drive-by-wire system (20) of the automated vehicle (2), to accordingly steer the latter. The invention is further directed to related systems and computer program products.

Systems and methods of performing localization of a vehicle are provided. The system receives data comprising one or more of: raw images from one or more vision sensors, motion sensor data, or lane-level map information. The system determines, based on the data, a lane in which the vehicle is located and lateral displacement of a center of the vehicle relative to a center of the lane.

A method for implementing autonomous driving and a vehicle-mounted control system includes: obtaining a first signal from at least one first sensor fixedly arranged at a specific location, where the first signal indicates static travelling information related to a lane; obtaining, from a vehicle-mounted sensor, a second signal indicating dynamic travelling information related to a vehicle travelling road; and establishing a travelling route corridor on the basis of at least one of the first signal and the second signal.

Systems and methods are disclosed for identifying landmarks. A method for identifying a landmark may include initiating identification of a landmark based on one or more images from a camera, for use in autonomous vehicle navigation, the landmark including a traffic sign; initiating updating a road model with a location of the landmark; and initiating distribution of the road model with the location of the traffic sign to a plurality of autonomous vehicles.

An endogenic protection method for function security and network security of a sensing and decision-making module of an intelligent connected vehicle, including the following steps: designing and implementing four sensing and decision-making units having the same function and a certain degree of heterogeneity, and deploying same on a vehicle; the four units respectively deducing real-time vehicle control decision-making results according to sensed information; an arbiter performing accurate arbitration and approximate arbitration according to decision-making results of three of the sensing and decision-making units; and finally, according to different decision-making results of the arbiter, the vehicle executing a CAN command, or replacing an anomalous unit and performing arbitration again, or operating according to a failure mode until the vehicle stops. Said method provides, by using the idea of endogenic security, an intelligent connected vehicle with a new solution for enhancing function security and network security in an integrated manner.