A method for supporting autonomous driving of an autonomous vehicle includes detecting, by an in-vehicle internet-of-things (IoT) platform of the autonomous vehicle, a vulnerable road user (VRU) having a mobile device in a vicinity of the autonomous vehicle. A mobility application runs on the mobile device of the VRU and sends VRU-specific data to the in-vehicle IoT platform of the autonomous vehicle. The VRU is detected based on the VRU-specific data and/or in-vehicle sensor data of the autonomous vehicle. The method further includes determining, by the in-vehicle IoT platform, a movement intention prediction based on the VRU-specific data. The movement intention prediction is computed by use of a machine learning model. The VRU-specific data of the mobile device are provided as input data for the machine learning model. In addition, the method includes performing an autonomous driving decision for the autonomous vehicle based on the movement intention prediction.

The present disclosure is directed to deviating from a planned path for an autonomous vehicle. In particular, a computing system comprising one or more computing devices physically located onboard an autonomous vehicle can identify one or more boundaries at least in part defining a lane in which the autonomous vehicle is traveling along a path of a planned route. Responsive to identifying one or more obstructions ahead of the autonomous vehicle along the path, the computing system can: determine one or more deviations from the path that would result in the autonomous vehicle avoiding the obstruction(s) and at least partially crossing at least one of the one or more boundaries; and generate, based at least in part on the deviation(s), a motion plan instructing the autonomous vehicle to deviate from the path such that it avoids the obstruction(s) and continues traveling along the planned route.

An autonomous vehicle includes sensor subsystem(s) that output a sensor signal. A perception subsystem (i) detects an agent in a vicinity of the autonomous vehicle and (ii) generates a motion signal that describes at least one of a past motion or a present motion of the agent. An intention prediction subsystem processes the sensor signal to generate an intention signal that describes at least one intended action of the agent. A behavior prediction subsystem processes the motion signal and the intention signal to generate a behavior prediction signal that describes at least one predicted behavior of the agent. A planner subsystem processes the behavior prediction signal to plan a driving decision for the autonomous vehicle.

A system for evaluating a risk value associated with an object on the road and a method for the same are provided. The method may include detecting, by a plurality of sensors, an object on a road that a vehicle travels, wherein each sensor of the plurality of sensor is configured to detect different types of the object; after detecting the object on the road, classifying, by a processor, the object into an object type; identifying, by the processor, a plurality of maneuvering options of the vehicle corresponding to the object type; calculating, by the processor, risk values of each maneuvering option; and selecting, by the processor, a maneuvering option, wherein a risk value of the selected maneuvering option is equal to or less than a predetermined risk value.

A risk potential field for a risk avoidance control (deceleration control) includes an obstacle potential field. The obstacle potential field is the risk potential field in which a risk value is maximum at a position of an object and decreases as a distance from the object increases. A search range is a range between the position of the object and a position away from the object by a predetermined gap. A local minimum point (valley) of the risk potential field is searched for within the search range. The deceleration control is executed based on a positional relationship between the local minimum point and the object. When a plurality of the local minimum points is present in the search range, a local minimum point that is the closest to the object is selected.

A driving support apparatus (10) executes collision prevention control for avoiding collision with the object when a possibility of a vehicle (VA) colliding with an object based on object information (e.g., distance, direction, and relative speed) acquired by a millimeter wave radar device (21) and a camera device (22) is high. Further, the driving support apparatus does not execute the collision prevention control when an accelerator pedal operation amount is equal to or larger than a stop threshold value. However, the driving support apparatus executes the collision prevention control even when the accelerator pedal operation amount is equal to or larger than the stop threshold value within a specific period of from a start point at which a predetermined erroneous operation condition is satisfied, to an end point, which is a time point after a predetermined consideration period has elapsed since the erroneous operation condition has no longer been satisfied.

A driving support apparatus according to the invention estimates the position of a moving body by controlling a position estimation unit when the tracking-target moving body leaves a first area or a second area to enter a blind spot area and detects the position of the moving body by controlling a position detection unit when the moving body leaves the blind spot area to enter the first area or the second area. In this manner, the trajectory of the tracking-target moving body is calculated so that the trajectory of the moving body detected in the first area or the second area and the trajectory of the moving body estimated in the blind spot area are continuous to each other and driving support is executed based on the calculated trajectory of the tracking-target moving body.

One or more techniques and/or systems are provided for notifying drivers to assume manual vehicle control of vehicles. For example, sensor data is acquired from on-board vehicles sensors (e.g., radar, sonar, and/or camera imagery of a crosswalk) of a vehicle that is in an autonomous driving mode. In an example, the sensor data is augmented with driving condition data aggregated from vehicle sensor data of other vehicles (e.g., a cloud service collects and aggregates vehicle sensor data from vehicles within the crosswalk to identify and provide the driving condition data to the vehicle). The sensor data (e.g., augmented sensor data) is evaluated to identify a driving condition of a road segment, such as the crosswalk (e.g., pedestrians protesting within the crosswalk). Responsive to the driving condition exceeding a complexity threshold for autonomous driving decision making functionality, a driver alert to assume manual vehicle control may be provided to a driver.

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one vehicle-induced occlusion zone in an environment of the host vehicle; and cause a navigational change for the host vehicle based, at least in part, on a size of a target vehicle that induces the identified occlusion zone.

A lifeform transmission system is used to locate a lifeform wanting to be identified as a lifeform for collision avoidance. The lifeform transmission system comprises a lifeform vitals detector, a GPS transmitter and various other electrical circuits. The lifeform transmitter is worn on a limb or neck of the lifeform. If the lifeform does not wear a transmitter no signal is emitted. The collision avoidance system is housed inside the vehicle and receives communication from invention. If any lifeform is positioned in the path or approaching the path that the ground vehicle is traveling, the vehicle's collision avoidance system is advised to avoid the collision to enhance the accuracy of the collision avoidance system. This invention complements the existing collision avoidance system and enhances the accuracy of vehicle systems by providing an input from the lifeform, making detection easier.