A method for determining event data including: sampling a first data stream within a first time window at a first sensor of an onboard vehicle system coupled to a vehicle, extracting interior activity data from the first data stream; determining an interior event based on the interior activity data; sampling a second data stream within a second time window at a second sensor of the onboard vehicle system; extracting exterior activity data from the second image stream; determining an exterior event based on the exterior activity data; correlating the exterior event and the interior event to generate combined event data; automatically classifying the combined event data to generate an event label; and automatically labeling the first time window of the first data stream and the second time window of the second data stream with the combined event label to generate labeled event data.

Disclosed is a method of providing driving information of an autonomous vehicle performed by an apparatus that includes a first sensor unit, a second sensor unit, a processor, and a projection unit. The method includes: acquiring, by the first sensor unit, surroundings information of the autonomous vehicle; acquiring, by the second sensor unit, information on an occupant on board the autonomous vehicle; generating, by the processor, main driving information from the surroundings information of the autonomous vehicle acquired by the first sensor unit; determining, by the processor, the main driving information to be provided to the occupant from among the main driving information based on the information on the occupant; and providing, by the projection unit, the main driving information determined by the processor to the occupant of the autonomous vehicle.

The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a driver-assist object detection system is provided for a vehicle. One or more processing devices associated with the system receive at least two images from a plurality of captured images via a data interface. The device(s) analyze the first image and at least a second image to determine a reference plane corresponding to the roadway the vehicle is traveling on. The processing device(s) locate a target object in the first two images, and determine a difference in a size of at least one dimension of the target object between the two images. The system may use the difference in size to determine a height of the object. Further, the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold.

Systems and methods of deep neural network based parking assistance is provided. A system can receive data sensed by one or more sensors mounted on a vehicle located at a parking zone. The system generates, from a first neural network, a digital map based on the data sensed by the one or more sensors. The system generates, from a second neural network, a first path based on the three-dimensional dynamic map. The system receives vehicle dynamics information from a second one or more sensors located on the vehicle. The system generates, with a third neural network, a second path to park the vehicle based on the first path, vehicle dynamics information and at least one historical path stored in vehicle memory. The system provides commands to control the vehicle to follow the second path to park the vehicle in the parking zone.

When there is a possibility of collision between a host vehicle and objects positioned in a detection region in front of the host vehicle, an ECU implements collision avoidance control for preventing the host vehicle from colliding with an object. The ECU detects the objects positioned in the detection region and judges whether, among the detected objects, there is an object that the host vehicle has overtaken and that is capable of moving within a prescribed range in the vehicle width direction of the host vehicle. If it is judged that the host vehicle is turning to the left or to the right after it has been judged that the host vehicle has overtaken an object, the actuation timing of the collision avoidance control is advanced.

A navigation system includes a processing device programmed to receive, from an image capture device, at least one image of an environment of the host vehicle; determine, based on at least one driving policy, a navigational action for accomplishing a navigational goal of the host vehicle; analyze the at least one image to identify a target vehicle; test the navigational action against at least one accident liability rule for determining potential accident liability for the host vehicle relative to the target vehicle; if the test indicates that potential accident liability exists for the host vehicle if the navigational action is taken, then cause the host vehicle not to implement the navigational action; and if the test indicates that no accident liability would result for the host vehicle if the navigational action is taken, then cause the host vehicle to implement the navigational action.

An accident liability tracking system includes a processing device programmed to receive, from an image capture device, an image representative of an environment of the host vehicle, to analyze the image to identify a target vehicle in the environment of the host vehicle, and determine one or more characteristics of a navigational state of the target vehicle. The device is further programmed to compare the characteristics of the navigational state of the target vehicle to at least one accident liability rule, store one or more values indicative of potential accident liability on the part of the identified target vehicle based on the comparison of the characteristics of the navigational state of the identified target vehicle to the at least one accident liability rule, and output the one or more values, after an accident between the host vehicle and a target vehicle, for determining liability for the accident.

According to an embodiment, an information processing device includes a memory and one or more hardware processors electrically coupled to the memory and configured to function as a change unit, and a display controller. The change unit is configured to change a reference path to a position at a lateral distance when the lateral distance obtained from lateral environmental information indicating a lateral environment of the reference path referred to as a scheduled running path of a moving body is larger than a distance from a lateral end to a center of a running region of the moving body. The display controller is configured to display display information including the reference path on a display unit.

Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.