A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

The disclosure provides an environment perception device and method of a mobile vehicle. The environment perception device includes a camera module, a LiDAR module, a database and a processing circuit. The camera module photographs a field near the mobile vehicle to generate a three-dimensional (3D) image frame. The LiDAR module scans the field to generate a 3D scanned frame. The processing circuit fuses the 3D image frame and the 3D scanned frame to generate 3D object information. The processing circuit compares the 3D object information with a 3D map in the database to determine whether an object is a static object. The processing circuit performs an analysis and calculation on the 3D object information to obtain movement characteristics of the object when the object is not the static object, and skips the analysis and calculation on the 3D object information when the object is the static object.

A method of moving an autonomous vehicle to a safe zone after an accident is provided. The method includes: setting a threshold which is a criterion for determining a failure of a chassis system; determining whether a failure occurs by using the threshold; determining a control mode of twin clutches or a braking system; designating avoidance speed level in accordance with whether a following vehicle approaches; and setting a target trajectory to a safe zone and then generating braking torque on left and right wheels or controlling distribution of driving torque through the twin clutches in order to move the autonomous vehicle at the avoidance speed along the target trajectory.

Provided is a vehicle tracking device capable of appropriately tracking a vehicle. The vehicle tracking device is a vehicle tracking device that tracks a target vehicle that travels at the periphery of a host vehicle. The vehicle tracking device includes: an estimation unit that estimates a rectangular frame approximating an external shape of the target vehicle on the basis of past data; and a specifying unit that specifies an advancing direction of the target vehicle on the basis of layout of contour data composed of a plurality of pieces of detection point data, which is detected by a sensor that is mounted to the host vehicle and detects a relative position of a detection target with respect to the host vehicle as the detection point data, and indicates a contour of the target vehicle, with respect to a reference side that is a side on the host vehicle side among sides of the rectangular frame along a vehicle width direction of the rectangular frame.

An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.

Provided is a vehicle braking support device. The braking support device includes: detection units for detecting a state around a host vehicle; a braking support control unit for executing braking support by a braking device according to the detected state; and a vehicle stop control unit for maintaining a stopped state of a host vehicle after the host vehicle is stopped by the braking support control unit, and for releasing the stopped state of the host vehicle after a predetermined period has elapsed. The vehicle stop control unit, in a case where by using the detected state it is determined that it is desirable to maintain the stopped state of the host vehicle beyond the predetermined period, the vehicle stop control unit does not release the stopped state of the host vehicle until an operation by a driver is detected.

A travel assistance method and a travel assistance device for a vehicle is capable of avoiding any risk that may arise. The method includes obtaining a risk potential of an object detected by the vehicle, associating the risk potential of the object with an encounter location at which the object is encountered, accumulating the risk potential at the encounter location, and using the accumulated risk potential to obtain a primary estimated risk potential of the object predicted to be encountered at the encounter location. The primary estimated risk potential is lower than the risk potential obtained when detecting the object. The method further includes obtaining a secondary estimated risk potential using a predicted travel movement of another vehicle that avoids a risk due to the primary estimated risk potential, and when traveling at the encounter location again, autonomously controlling travel of the vehicle using the secondary estimated risk potential.

A method for determining passage of an autonomous vehicle includes: acquiring information about an intersection on a driving route of the autonomous vehicle, wherein the information about the intersection comprises lane data; acquiring a historical trajectory of an obstacle in the intersection within a specific time; acquiring, by matching the historical trajectory of the obstacle with center lines of respective lanes in the lane data, a lane with smallest matching error; and determining that the lane with the smallest matching error is in a passable state, wherein the lane with the smallest matching error is a lane where the obstacle is located.

An apparatus for controlling lane change is disclosed. The apparatus may include a weight detection unit sensing weight of freight, a safety space setting unit receiving sensed weight and setting a safety space required for a vehicle to safely move to a target lane from a current lane based on the sensed weight, a sensor unit sensing whether an object exists in the safety space using at least one sensor, and a control unit controlling the vehicle to move to the target lane within a preset time when there is no sensed object. A method of controlling lane change is also disclosed.

A trajectory prediction method and apparatus, a storage medium, and an electronic device are provided. In embodiments of this disclosure, according to a historical trajectory of a designated target and a historical trajectory of each obstacle, a historical interaction feature between the designated target and each obstacle is determined, and a motion trajectory of the designated target is predicted to obtain an initial predicted trajectory. A future interaction feature between each obstacle and the designated target is then determined according to the initial predicted trajectory and a planned trajectory of each obstacle. According to the future interaction feature, a final predicted trajectory of the designated target is obtained.