A method for controlling mechatronic systems is described herein. In accordance with one embodiment the method includes planning a nominal path for a mechatronic system using an automatic path planner, receiving information concerning one or more objects detected in the surrounding environment of the mechatronic system and calculating one or more occupancy sets corresponding to the one or more detected objects, and detecting whether the nominal path violates at least one of the one or more Occupancy Sets. In one embodiment, the occupancy sets may represent theoretic system states of the mechatronic system which are potentially occupied by the stationary and dynamic objects at a specific time. Furthermore, a corresponding control system is described.

A vehicle control device that autonomously controls a vehicle so as not to cause rapid deceleration that leads to a deterioration in ride quality. The vehicle control device controls first and second deceleration, means that reduce a speed at a deceleration rate large than a deceleration rate of the first deceleration means. The vehicle control device includes a blind spot area detecting unit that detects a blind spot area of a sensor that recognizes an external environment, and a blind spot object estimating unit that estimates a blind spot object that is a virtual moving body hidden in the blind spot area. When a vehicle approaches the blind spot area at a speed reduced by the first deceleration means, the vehicle is decelerated by the second deceleration means when a type of a moving body detected by the sensor is different from a type of the blind spot object.

An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

A vehicular vision system includes a forward-viewing camera disposed at a vehicle, and an electronic control unit (ECU). Electronic circuitry of the ECU includes an image processor for processing image data captured by the forward-viewing camera. The vehicular vision system, responsive to processing by the image processor of image data captured by the forward-viewing camera, detects a target vehicle. The vehicular vision system, responsive to detecting the target vehicle, predicts, using a machine learning model, a probability for each action of a set of actions, with each action in the set of actions representing a potential action by the target vehicle. The machine learning model includes at least one discrete latent variable and at least one continuous latent variable. The vehicular vision system, responsive to predicting the probability for each action, autonomously controls the equipped vehicle.

In one embodiment, a plurality of obstacles is sensed in an environment of an automated driving vehicle (ADV). One or more representations are formed to represent corresponding groupings of the plurality of obstacles. A vehicle route is determined in view of the one or more representations, rather than each and every one of the obstacles individually.

A method, a computer program, an apparatus, a transportation vehicle, and a traffic entity for updating an environmental model at a transportation vehicle. The method for a traffic entity and for updating a first environmental model at a transportation vehicle includes receiving information related to the first environmental model from the transportation vehicle wherein the first environmental model has at least first information on an object in an environment of the transportation vehicle and the first environmental model includes confidence information related to the first information. The method also includes obtaining information related to a second environmental model of the transportation vehicle at the traffic entity wherein at least second information on the object in the environment of the transportation vehicle and the second environmental model includes confidence information related to the second information.

Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P.sub.1, P.sub.2, P.sub.3) and arrival times of the vehicles (1, 18) at the discretization points (P.sub.1, P.sub.2, P.sub.3) are utilized to generate constraints for the model predictive control of the first vehicle (1).

A method predicts the trajectory of an ego vehicle travelling in a main lane. A lane change by the ego vehicle from the main lane to an adjacent lane is determined according to an estimate of the dynamic behavior of a group of vehicles travelling in the adjacent lane. The group of vehicles includes at least one main vehicle which is located near the ego vehicle and a secondary vehicle which is located behind the ego vehicle.

Disclosed are distributed computing systems and methods for controlling multiple autonomous control modules and subsystems in an autonomous vehicle. In some aspects of the disclosed technology, a computing architecture for an autonomous vehicle includes distributing the complexity of autonomous vehicle operation, thereby avoiding the use of a single high-performance computing system and enabling off-the-shelf components to be use more readily and reducing system failure rates.

A method is disclosed for evaluating a classifier used to determine a traffic light signal state in images. The method includes, by a computer vision system of a vehicle, receiving at least one image of a traffic signal device of an imminent intersection. The traffic signal device includes a traffic signal face including one or more traffic signal elements. The method includes classifying, by a traffic light classifier (TLC), a classification state of the traffic signal face using labeled images correlated to the received at least one image. The classification state controls an operation of the vehicle at the intersection. The method includes evaluating a performance of the classifying of the classification state generated by the TLC. The evaluation is a label-free performance evaluation based on unlabeled images. The method includes training the TLC based on the evaluated performance.