An obstacle avoidance method and apparatus for an autonomous driving vehicle is provided. The method includes: in response to determining that there is an obstacle in a preset driving path, sending obstacle information to a preset terminal device so that the preset terminal device displays the obstacle information in a display page thereof, the obstacle information including an image of the obstacle and location information; receiving obstacle category information sent by the preset terminal device and inputted according to the displayed obstacle information; and determining an obstacle avoidance instruction for the autonomous driving vehicle according to the obstacle category indicated by the category information.

A comfort-based self-driving planning method, including the steps of: a) establishing a relationship model of vibration road surface quality and driving comfort on the basis of a vehicle type; b) obtaining road ahead condition parameters, including abnormal condition information, road flatness and road surface anti-slide performance; c) obtaining the road ahead condition parameters, and adjusting a vehicle expected driving trajectory; d) respectively designing vehicle acceleration, deceleration and constant speed processes, and generating a speed change curve; e) optimising the speed change curve. By means of changeable road surface quality and vehicle vibration action mechanism analysis and image-based road surface anti-slide coefficient evaluation technology, a GIS and vehicle-road communication technology are used to acquire road condition parameters, and vehicle acceleration, deceleration and constant speed processes are respectively designed on the basis of changes in the parameters.

When one sensors fails and thus malfunctions during automatic parking of a vehicle, only the other sensor functions. Accordingly, depending on the behavior of the vehicle, the detection characteristics of the other sensor cause degradation in recognition of the vehicle, hindering the automatic parking from being continued. In processing S401, a determination is made whether or not an external recognition device, such as a camera or sonar, malfunctions. In processing S402, based on the determination on malfunction of the external recognition device, a determination is made whether or not to restrict vehicle speed or to restrict a path, with reference to restriction information for parking control. The restriction information for parking control provides information for restricting the vehicle speed in accordance with the malfunction of the camera, and for restricting the path in accordance with the malfunction of the sonar. Next, in processing S403, a determination is made whether or not the automatic parking is in progress. When the automatic parking is in progress, the process proceeds to processing S404 where vehicle speed control or the like is performed for the automatic parking in accordance with the malfunction of the external recognition device.

The present disclosure relates to systems and methods for host vehicle navigation. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the images to identify a target vehicle in the environment of the host vehicle; cause a navigational change of the host vehicle to signal to the target vehicle an intent of the host vehicle to make a subsequent navigational maneuver; analyze the images to detect a change in a navigational state of the target vehicle; determine a navigational action for the host vehicle; and cause an adjustment of a navigational actuator of the host vehicle in response to the determined navigational action for the host vehicle.

Systems and methods are provided for determining a lane assignment for an autonomous vehicle along a road segment. In one implementation, at least one image representative of an environment of the vehicle is received from a camera. The at least one image may be analyzed to identify at least one recognized landmark, and an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark may be determined. Moreover, a lane assignment of the vehicle along the road segment may be determined based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark.

A vehicle control system includes: a recognizer that recognizes a distribution state of obstacles in an advancement direction of a vehicle; a trajectory determiner that determines a target trajectory for each vehicle wheel of the vehicle on the basis of the distribution state of the obstacles recognized by the recognizer; and an automated driving controller that executes automated driving of the vehicle along the target trajectory determined by the trajectory determiner.

Techniques are provided for autonomous vehicle fleet management for reduced traffic congestion. A request is received for a vehicular ride. The request includes an initial spatiotemporal location and a destination spatiotemporal location. A processor is used to generate a representation of lane segments. Each lane segment is weighted in accordance with a number of other vehicles on the lane segment. A vehicle located within a threshold distance to the initial spatiotemporal location is identified such that the identified vehicle has at least one vacant seat. The processor is used to determine a route for operating the identified vehicle from the initial spatiotemporal location to the destination spatiotemporal location. The route includes one or more lane segments of the lane segments. An aggregate of weights of the one or more lane segments is below a threshold value. The received request and the determined route are transmitted to the identified vehicle.

Systems and methods are provided for distributing a crowdsourced sparse map for autonomous vehicle navigation. In one implementation, a method of generating a road navigation model for use in autonomous vehicle navigation may include receiving navigation information associated with a common road segment from a plurality of vehicles; storing the navigation information associated with the common road segment; generating at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information; and distributing the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating along the common road segment. The autonomous vehicle road navigation model may include at least one line representation of a road surface feature extending along the common road segment, and each line representation may representing a path along the common road segment substantially corresponding with the road surface feature.

A sensing system for a vehicle includes at least one radar sensor disposed at the vehicle and having a field of sensing exterior of the vehicle. The at least one radar sensor includes an array of multiple transmitting antennas and multiple receiving antennas. A control is responsive to the outputs of the at least one radar sensor and determines the presence of one or more other vehicles exterior the vehicle and within the field of sensing of the at least one radar sensor. Lane markers may be determined via a vision system of the equipped vehicle. The control determines range and relative lane position of a detected other vehicle relative to the equipped vehicle and determined lane markers, and the sensing system may anticipate a lane change, cut-in or merge intent of the detected other vehicle.

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.