Systems, methods, and non-transitory computer-readable media are provided for implementing a tracking camera system onboard an autonomous vehicle. Coordinate data of an object can be received. The tracking camera system actuates, based on the coordinate data, to a position such that the object is in view of the tracking camera system. Vehicle operation data of the autonomous vehicle can be received. The position of the tracking camera system can be adjusted, based on the vehicle operation data, such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion. A focus of the tracking camera system can be adjusted to bring the object in focus. The tracking camera system captures image data corresponding to the object.

The collision prediction apparatus is installed in a vehicle and predicts a collision between a moving object and the vehicle. The collision predict apparatus includes a vehicle path estimation section, a moving object path estimation section, an obstruction specifying section, a direction change information acquisition section, a moving object extraction area setting section, and a collision predict section. The moving object extraction area setting section sets, as a moving object extraction area, an area that is in the vicinity of an obstruction and is near an outer periphery of the obstruction facing a path of the vehicle after change of a traveling direction indicated by direction change information.

Disclosed is a vehicle. The vehicle generally includes a frame to support an engine and one or more supports, such as wheels, to support the frame. The engine may include an internal combustion power plant and a fuel supply system therefore.

A driving system for the automated driving for a motor vehicle is configured to determine a field of vision of at least one sensor of the motor vehicle, for at least one area in the surroundings of the motor vehicle which does not lie in the field of vision of the at least one sensor, to simulate, if appropriate, a virtual road user in the at least one area in the surroundings of the motor vehicle, to carry out an evaluation of action options in accordance with a possible virtual collision of the motor vehicle with the at least one virtual road user, to select one of these action options in accordance with the above, and to execute the selected action option.

Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

A vehicle controller and a method thereof are provided. The vehicle controller includes a processor that determines whether a driver has an intention to make a lane change using at least one of a steering angle condition, a gaze condition of the driver, or a driving pattern condition of the driver. The processor shares lane change information with a surrounding vehicle, when it is determined that the driver has the intention to make the lane change. The controller also has a storage storing data and an algorithm run by the processor.

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.

An autonomous vehicle navigates an intersection in which occlusions block the vehicle's ability to detect moving objects. The vehicle handles this by generating a phantom obstacle behind the occlusion. The vehicle will predict the speed of the phantom obstacle and use the predicted speed to assess whether the phantom obstacle may collide with the vehicle. If a collision is a risk, the vehicle will slow or stop until it confirms that either (a) the phantom obstacle is not a real obstacle or (b) the vehicle can proceed at a speed that avoids the collision. To determine which occlusions shield real objects, the system may use a rasterized visibility grid of the area to identify occlusions that may accommodate the object.

Methods and systems are provided for dynamically adjusting at least one setting of a lidar. In some aspects, a process can include steps for receiving, at an autonomous vehicle system, road condition data associated with an autonomous vehicle, generating, by the autonomous vehicle system, instructions to adjust the at least one setting of the lidar of the autonomous vehicle based on the road condition data, and providing, by the autonomous vehicle system, the instructions to adjust the at least one setting of the lidar to the lidar, the instructions including updating firmware of the lidar to adjust the at least one setting of the lidar.

A driving assistance system includes a processor, a storage device, and an output device. The storage device retains traveling environment information indicating a traveling environment of a vehicle and driving characteristic information indicating driving characteristics of a driver of the vehicle. The processor generates a message corresponding to the traveling environment of the vehicle based on the traveling environment information, and generates a message corresponding to the driving characteristics of the driver of the vehicle based on the driving characteristic information. The output device outputs the message corresponding to the traveling environment and the message corresponding to the driving characteristics in different aspects.