A prediction method and apparatus for an autonomous driving manual takeover, and a system are provided. One example method includes: A first vehicle sends a first message to a second vehicle when detecting that the first vehicle has a manual takeover requirement, where the first message includes information about a first location of the first vehicle, and the information about the first location is used to indicate a location of the first vehicle when the first vehicle detects that the first vehicle has the manual takeover requirement.

A system and method for slowing a vehicle. Road conditions around the vehicle are monitored, and determined if those road conditions are hazardous. An engine control unit is informed of the hazardous road conditions and alters the operation of the engine control unit in response to the hazardous road conditions. When an operator of the vehicle desires to slow the vehicle down, an indication is received indicating the intent to slow the vehicle down. The vehicle is then slowed based upon the altered operation of the engine control unit by applying a vacuum to increase a manifold vacuum of the engine.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

Systems, methods, and computer program products to enhance the situational competency and/or the safe operation of a vehicle, when operating at least partially in an autonomous mode, as a support vehicle for one or more on-road persons engaged in a training or competitive cycling, running, and/or walking activity on a predetermined travel route at a predetermined pace.

Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

A driving assist ECU determines that a current situation is a specific situation where it is predicted that there is no object that is about to enter an adjacent lane from an area outside of a host vehicle road on which a host vehicle is traveling, when a road-side object is detected at a part around an edge of the adjacent lane, and/or when a white line painted to define the adjacent lane is detected at the part around the edge of the adjacent lane and no object near the detected white line is detected. The driving assist ECU does not perform a steering control for avoiding a collision, the steering control for letting the vehicle enter the adjacent lane, when it is not determined that the current situation is the specific situation.

An autonomous driving control apparatus installable in a vehicle includes a path determining section, an obstacle determining section that determines whether an obstacle on the planned driving path is a passage acceptable obstacle or a passage unacceptable obstacle, the passage acceptable obstacle being previously set as an obstacle that the vehicle is allowed to come into contact with while passing, the passage unacceptable obstacle being previously set as an obstacle that the vehicle is not allowed to come into contact with while passing, and a control instructing section that gives an instruction of control to a maneuver controller to perform at least one of controlling a speed of the vehicle and controlling a steering of the vehicle to control a maneuver of the vehicle. If the obstacle is determined to be the passage acceptable obstacle, the control instructing section gives an instruction of the control to pass over the obstacle.

A method detects a risk of collision between a motor vehicle and a secondary object located in traffic lanes adjacent to the main traffic lane of the vehicle, in the event of a lane change by the vehicle, which involves detecting objects in a predetermined danger zone, and estimating a time-to-collision between the vehicle and a detected object. Detecting objects in a danger zone involves: calculating the actual distance between the vehicle and each object detected by the radar, the actual distance corresponding to the length of an arc between two points; determining a danger zone as a function of lines of the main traffic lane and a width of the main traffic line; and checking, for each object detected by the radar, whether its coordinates are inside the predetermined danger zone.

System and techniques for test scenario verification, for a simulation of an autonomous vehicle safety action, are described. In an example, measuring performance of a test scenario used in testing an autonomous driving safety requirement includes: defining a test environment for a test scenario that tests compliance with a safety requirement including a minimum safe distance requirement; identifying test procedures to use in the test scenario that define actions for testing the minimum safe distance requirement; identifying test parameters to use with the identified test procedures, such as velocity, amount of braking, timing of braking, and rate of acceleration or deceleration; and creating the test scenario for use in an autonomous driving test simulator. Use of the test scenario includes applying the identified test procedures and the identified test parameters to identify a response of a test vehicle to the minimum safe distance requirement.