The present invention extends to methods, systems, and computer program products for using geofences to restrict vehicle operation. Aspects of the invention include creating dynamic geofences and limiting vehicle movements (e.g., speed, acceleration, steering, etc.) within and in the vicinity of the dynamic geofences to protect pedestrians from physical harm. In general, radio devices track people in an area by counting the number of devices and calculating the number of people based on average number of devices per person. Using count and location data, a geofence is created when population or population density within an area exceeds a threshold. The geofence can be sent to vehicles to restrict vehicle operation, for example, slowing down or stopping the vehicle, within and around the geofence.

A travel control for a vehicle includes specifying a pedestrian crosswalk through which a subject vehicle is expected to pass as a first pedestrian crosswalk, estimating a position on the first pedestrian crosswalk through which the subject vehicle passes as a crossing position in the length direction of the first pedestrian crosswalk, specifying another pedestrian crosswalk located within a predetermined distance from the crossing position and located close to the first pedestrian crosswalk as a second pedestrian crosswalk, setting an area including the first pedestrian crosswalk and the second pedestrian crosswalk as a detection area of a detector detecting an object around the subject vehicle, detecting a moving object in the detection area using the detector, and controlling travel of the subject vehicle on the basis of a detection result of the detector.

A method for controlling a vehicle includes: determining whether map data for a first geographic section is stored in memory; and based on the map data having been stored: generating, through an object detection device and when the vehicle drives through the first geographic section by user control, first object information regarding vehicle surroundings; and storing first map data based on the first object information. The method further includes: based on the first map data having been stored, generating, based on the stored first map data, a driving route and driving control information for the first geographic section; generating, through the object detection device and when the vehicle drives along the driving route through the first geographic section, second object information regarding vehicle surroundings; and updating and storing the first map data based on the second object information.

An approach is described for operating a driver assistance system that is used to predict a movement of at least one living object in the surroundings (17) of the motor vehicle. The approach includes storing motion models characterizing movements for a combination of object classes; receiving measurement data relating to the surroundings; recognizing the living object and at least one other object in the surroundings and determining a position of the objects in relation to each other; identifying the object classes of the known objects; for the living object developing an equation of motion at least according to the respective position of the living object in relation to the other object as well as the motion model stored for the combination of the identified object classes; and predicting the movement on the basis of the equation of motion; and operating the driver assistance system taking into account the predicted movement.

Various technologies described herein pertain to labeling sensor data generated by autonomous vehicles. A computing device identifies candidate path plans for an object in a driving environment of an autonomous vehicle based upon sensor data generated by sensor systems of the autonomous vehicle. The sensor data is indicative of positions of the object in the driving environment at sequential timesteps in a time period. Each candidate path plan is indicative of a possible maneuver being executed by the object during the time period. The computing device generates a weighted directed graph based upon the candidate path plans. The computing device determines a shortest path through the weighted directed graph. The computing device assigns a maneuver label to the sensor data based upon the shortest path, wherein the maneuver label is indicative of a maneuver that the object executes during the time period.

Systems of an electrical vehicle and the operations thereof are provided.

The likelihood of a collision of a vehicle colliding with an object in front of an own vehicle is determined, and an emergency braking control for avoiding a collision with the object is started in accordance with the determination results. A determination is made as to whether travel environment conditions have been established, from the location at which the vehicle is currently travelling, the situation behind the vehicle, and, the travel state of the vehicle, and the braking control is released when the likelihood of a collision dropped to a predetermined safety level during the period from the start of the emergency braking control until the own vehicle stops, and when the travel environment conditions have been established.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

A driving support apparatus according to the invention estimates the position of a moving body by controlling a position estimation unit when the tracking-target moving body leaves a first area or a second area to enter a blind spot area and detects the position of the moving body by controlling a position detection unit when the moving body leaves the blind spot area to enter the first area or the second area. In this manner, the trajectory of the tracking-target moving body is calculated so that the trajectory of the moving body detected in the first area or the second area and the trajectory of the moving body estimated in the blind spot area are continuous to each other and driving support is executed based on the calculated trajectory of the tracking-target moving body.

A method and a system for predicting a near future path and an associated output control signal for a vehicle. Prediction sensor data, vehicle driving data, and road data are collected. An input control signal indicative of an intended driving action is received. The sensor data and the vehicle driving data are pre-processed to provide a set of object data comprising a time series of previous positions of a respective object relative the vehicle, a time series of the previous headings of the object, and a time series of previous velocities of the object. The object data, the road data, the vehicle driving data, the control signal, and the sensor data are processed in a deep neural network. Based on the processing in the deep neural network, a predicted path output and an output control signal are provided.