A surrounding environment information generating unit acquires surrounding position information indicating a measured position of a side wall including a side wall of an evacuation spot, and generates measured map information for the side wall using the surrounding position information. When driving operation by a driver cannot be performed, a corrected map information generating unit calculates a correction value obtained by multiplying a difference between a distance from a traveling center line of a host vehicle to the side wall in base map information and that in the measured map information by a reliability, and generates corrected map information obtained by applying the correction value to the base map information. A route generating unit generates an evacuation travel route with the evacuation spot as a destination using the corrected map information. A vehicle control unit outputs a vehicle speed command for the host vehicle using the evacuation travel route.

An apparatus for controlling a vehicle includes a memory, a network interface, and a processor that determines whether the vehicle is in a bypass situation in which the vehicle has to bypass, calculates a plurality of bypass paths capable of replacing a previously set travel path in the bypass situation, evaluates the plurality of bypass paths, selects one bypass path based on the evaluation of the plurality of bypass paths, and performs control on the vehicle such that the vehicle travels along the selected bypass path.

Methods and systems for training a model and automated motion include learning Markov decision processes using reinforcement learning in respective training environments. Logic rules are extracted from the Markov decision processes. T reward logic neural network (LNN) and a safety LNN are trained using the logic rules extracted from the Markov decision processes. The reward LNN and the safety LNN each take a state-action pair as an input and output a corresponding score for the state-action pair.

Provided are methods for generating notifications indicative of unanticipated actions. Data associated with a current trajectory of an autonomous vehicle, at least one constraint, data associated with historical data for a user using the autonomous vehicle from an expectation database, and data associated with a context that represents a relationship between the current trajectory, at least one constraint, and historical driving data is received. A model is deployed to determine, in real-time and based on the current trajectory, the at least one constraint, and the data associated with the context that a particular action by the autonomous vehicle is classified as an unanticipated autonomous vehicle action. The current trajectory, the at least one constraint, and the data associated with the context are analyzed within a predetermined range of time including a timestamp of the unanticipated autonomous vehicle action to determine a reason for occurrence of the unanticipated autonomous vehicle action. A notification is generated that includes the reason, wherein an intensity of the notification is based on, at least in part, the deviation.

A vehicle for collecting image data of a target object for generating a classifier. The vehicle includes an image sensor and an electronic processor. The electronic processor is configured to determine a plurality of potential trajectories of the vehicle, determine, for each of the plurality of potential trajectories of the vehicle, a total number of views including the target object that would be captured by the image sensor as the vehicle moved along the respective trajectory, and determine a key trajectory of the vehicle from the plurality of potential trajectories based on the total number of views including the target of the key trajectory.

A vehicle control system that acquires position information of a vehicle in a driving state of a manually driven state or a remotely operated driven state; that stores a pre-traveled travel route of a vehicle in the manually driven state or the remotely operated driven state based on the acquired position information; and that creates a travel route on which a vehicle is caused to travel in an autonomously driven state using the stored travel route.

A steering method for an autonomously steered vehicle having a hybrid steering system, including: identifying a malfunction during an autonomous driving procedure, wherein the hybrid steering system includes a hydraulic steering assistance system, an electromechanical steering assistance system and a control unit for monitoring and controlling the autonomous driving procedure of the vehicle, and switching the steering task by the control unit to a steering braking procedure, by which the vehicle is steered by braking at least one wheel, wherein due to the braking force that is acting on the scrub radius, a steering torque is produced that causes the wheels to turn. Also described is a related a servo-assisted steering assembly.

Techniques for determining a response of a simulated vehicle to a simulated object in a simulation are discussed herein. Log data captured by a physical vehicle in an environment can be received. Object data representing an object in the log data can be used to instantiate a simulated object in a simulation to determine a response of a simulated vehicle to the simulated object. Additionally, one or more trajectory segments in a trajectory library representing the log data can be determined and instantiated as a trajectory of the simulated object in order to increase the accuracy and realism of the simulation.

A reporting device includes an emergency reporting section that sends, to a response system via a communication control section, first emergency information including at least one of no-occupant information indicating that no occupant is present in a moving body and autonomous driving mode setting information indicating that the moving body is set in an autonomous driving mode when recognizing that a shock has occurred in the moving body based on shock occurrence information while it is recognized by a driving mode recognizing section that the moving body is set in the autonomous driving mode and it is recognized by an occupant recognizing section that no occupant is present in the moving body.

A method for controlling emergency stop of an autonomous vehicle is provided. The method includes: controlling emergency stop of an autonomous vehicle capable of recognizing a stop request from an emergency vehicle, determining whether a current situation is an emergency situation or a general situation, and performing a procedure corresponding to each situation to stop the autonomous vehicle based on each situation.