A driving assistance control device includes an active pedal configured to control a driving and braking force of a vehicle, an electronic control unit configured to detect a potential risk area in which an obstacle entering a scheduled traveling route of the vehicle is likely to be present, and determine a reference speed at which contact between the vehicle and the obstacle can be avoided even when the obstacle enters the scheduled traveling route of the vehicle from the detected potential risk area based on a positional relationship between the vehicle and the potential risk area, and a force feedback unit configured to apply an assistance reaction force in a direction in which the amount of manipulation is reduced, to the active pedal when a current speed of the vehicle exceeds the reference speed.

An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

A mobile ecosystem includes an autonomous control system in a vehicle. The autonomous control system receives user information from at least one of a user device or the vehicle and identifies a trajectory plan and a vehicle configuration based on the user information. The trajectory plan identifies a departure time for the vehicle to depart a starting location, an arrival time for the vehicle to arrive at a destination location, and a travel route for the vehicle to traverse at least partially between the starting location and the destination location. The trajectory plan may identify an entry time for the user to enter the vehicle. The vehicle configuration identifies travels settings for components in at least one of dynamic systems or interior systems of the vehicle. The autonomous control system causes the components to be configured according to the travel settings and causes the vehicle to traverse the travel route.

This document describes methods by which a system determines a pickup/drop-off zone (PDZ) to which a vehicle will navigate to perform a ride service request. The system will define a PDZ that is a geometric interval that is within a lane of a road at the requested destination of the ride service request by: (i) accessing map data that includes the geometric interval; (ii) using the vehicle's length and the road's speed limit at the destination to calculate a minimum allowable length for the PDZ; (iii) setting, start point and end point boundaries for the PDZ having an intervening distance that is equal to or greater than the minimum allowable length; and (iv) positioning the PDZ in the lane at or within a threshold distance from the requested destination. The system will then generate a path to guide the vehicle to the PDZ.

A vehicular control system determines a planned path of travel for a vehicle along a traffic lane in which the vehicle is traveling on a road. The system determines a respective target speed for waypoints along the planned path that represents a speed the vehicle should travel when passing through the respective waypoint. The system determines a speed profile for the vehicle to travel at as the vehicle travels along the planned path, with at least two different speeds being based on a difference in target speeds of at least two consecutive respective waypoints of the plurality of waypoints. The system determines an acceleration profile for the vehicle to follow as it changes from one speed to another speed of the speed profile. The system controls the vehicle to maneuver the vehicle along the planned path in accordance with the determined speed and acceleration profiles.

Performance anomalies in autonomous vehicle can be difficult to identify, and the impact of such anomalies on systems within the autonomous vehicle may be difficult to understand. In examples, systems of the autonomous vehicle are modeled as nodes in a probabilistic graphical network. Probabilities of data generated at each of the nodes is determined. The probabilities are used to determine capabilities associated with higher level functions of the autonomous vehicle.

A vehicular collision avoidance system includes a forward-viewing camera, a rearward-viewing camera, a rearward-sensing non-vision sensor and an electronic control unit. The vehicular collision avoidance system detects vehicles present forward and/or rearward of the equipped vehicle. Responsive to at least one selected from the group consisting of (i) data processing of image data captured by the rearward-viewing camera and (ii) data processing of sensor data captured by the rearward-sensing non-vision sensor, the vehicular collision avoidance system detects another vehicle approaching the equipped vehicle from the rear, determines that the other vehicle is traveling in the same traffic lane as the equipped vehicle, determines speed difference between the vehicles, and determines distance from the equipped vehicle to the other vehicle. Based on such determinations, the system determines that impact with the equipped vehicle by the other vehicle is imminent.

A computer-implemented method for creating a virtual vehicle environment includes: receiving data of a real vehicle environment; generating a first feature vector representing a respective real object by applying a second machine learning algorithm to the respective real object and storing the first feature vector; providing a plurality of stored second feature vectors representing synthetically generated objects; identifying a second feature vector having a greatest degree of similarity to the first feature vector; selecting the identified second feature vector and retrieving a stored synthetic object that is associated with the second feature vector and that corresponds to the real object or procedurally generating the synthetic object that corresponds to the real object, depending on the degree of similarity of the identified second feature vector to the first feature vector; and integrating the synthetic object into a predetermined virtual vehicle environment.

There are provided an object recognition apparatus that raises the recognition accuracy for a surrounding object and a vehicle control apparatus, and an object recognition method and a vehicle control method. An object recognition apparatus receives object data, which is a state value of the object, from a first sensor for detecting a surrounding object; compares estimation data obtained through estimation of a state value of the object, based on recognition data calculated in a past period, with the object data, and determines whether or not the object data is data in a low-resolution state; then, in accordance with the determination result, calculates the state value of the object by use of object data and estimation data and then generates the state value as recognition data, so that the recognition accuracy for an object is raised.

Redundant environment perception tracking for automated driving systems. One example embodiment provides a surveillance system, the system including a plurality of sensors, a memory, and an electronic processor. The electronic processor is configured to receive, from the plurality of sensors, environmental information of a common field of view, generate, based on the environmental information, a plurality of hypotheses regarding an object within the common field of view, the plurality of hypotheses including at least one set of hypotheses excluding the environmental information from at least one sensor of a first sensor type, determine, based on a subset of the plurality of hypotheses, an object state of the object, wherein the subset includes the at least one set of hypotheses excluding the environmental information from the at least one sensor of the first sensor type, and track the object based on the object state that is determined.