Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that determine yield behavior for an autonomous vehicle. An agent that is in a vicinity of an autonomous vehicle can be identified. An obtained crossing intent prediction characterizes a predicted likelihood that the agent intends to cross a roadway during a future time period. First features of the agent and of the autonomous vehicle are obtained. An input that includes the first features and the crossing intent prediction is processed using a machine learning model to generate an intent yielding score that represents a likelihood that the autonomous vehicle should perform a yielding behavior due to the intent of the agent to cross the roadway. From at least the intent yielding score, an intent yield behavior signal is determined and indicates whether the autonomous vehicle should perform the yielding behavior prior to reaching the first crossing region.

Disclosed is a vehicle path control apparatus including a memory provided to store danger driving vehicle data, a communication device provided to receive road information from an external server, and a processor configured to identify a danger driving vehicle driving on a road based on the road information and the danger driving vehicle data, select an unmanned vehicle driving within a preset distance from the danger driving vehicle based on the road information, generate a driving path of the unmanned vehicle for obstructing a course of the danger driving vehicle based on an expected path of the danger driving vehicle, and control the communication device to transmit information on the driving path to the unmanned vehicle.

A method of assisting a motor vehicle driven in an at least semiautomated manner, for passing through a tunnel. The method includes: receiving surrounding-area signals that represent an area, which surrounds the motor vehicle, and of which at least a part includes a tunnel; receiving safety condition signals, which represent at least one safety condition that must be satisfied, so that the motor vehicle may be assisted from outside of the motor vehicle while passing through the tunnel; checking if the at least one safety condition is satisfied; generating data signals, which represent data suitable for assisted traversal of the tunnel by the motor vehicle, based on the surrounding-area signals, and based on a result of whether the at least one safety condition is satisfied; outputting the generated data signals. A device, a computer program, and a machine-readable storage medium are also described.

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example system for an AV includes obtaining, by a computer located in the AV, an image from a camera located on the AV, where the image characterizes an area towards which the AV is driven on a lane on a road or a highway; determining, from the image, that a pedestrian or a cyclist is located next to the lane on the road or the highway; and in response to the determining, performing driving operations on the AV such as steering from a center of the lane to a first side of the lane that is away from the center of the lane and away from a location of the pedestrian or the cyclist, and/or slowing down the AV in response to certain conditions.

A first obstacle colliding with the ADV is detected. A minimum deceleration that is required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of a moving direction is determined. A brake command is generated based on the minimum deceleration. Then, the brake command is applied to the ADV, such that the ADV avoids collision with the second obstacle and softens an impact of the collision with the first obstacle.

A vehicle control method is provided such that when a host vehicle is stopped at a front of a vehicle line of vehicles in accordance with a stop signal of a traffic light at an intersection, the engine is stopped by using an idle stop control. When either a left-turn or a right-turn will be made after the traffic light changes to a go signal, a presence or an absence of a traveling body, which is stopped on a side of or behind the host vehicle in a direction from which the host vehicle is turning, is detected during the stop signal. Upon determining the traveling body is stopped on the side of host vehicle, restarting of the engine is placed on standby even when the traffic light turns to the go signal, and the engine is restarted in accordance with a behavior of the traveling body.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

A motion planner performs motion planning with collision assessment, using a motion planning lattice that represents configuration states of a primary agent (e.g., autonomous vehicle) as nodes and transitions between states as edges. The system may assign cost values to edges, the cost values representing probability or likelihood of collision for the corresponding transition. The cost values may additionally or alternatively represent a severity of collision, for example generated via a parametric function with two or more parameters and one or more weights. A primary agent and/or dynamic obstacles may be represented as respective oriented bounding boxes. Some obstacles (e.g., road markings, edge of road) may be represented as curves. A trajectory of a primary agent and/or dynamic obstacle may be represented by respective sets of fitted polynomial functions, edges on the planning graph, which represent transitions in states of the primary agent, the system sets value representing a probability of collision, and optionally representing a severity of the collision. The system then causes the actuator system of the primary agent to implement a motion plan with the applicable identified path based at least in part on the optimization.

According to various embodiments, a method for operating a vehicle may include determining a vehicular area having traffic conditions or characteristics different from traffic conditions of a current or previous location of the vehicle; obtaining traffic and driving information for the determined vehicular region; changing or updating one or more of driving model parameters of a safety driving model during operation of the vehicle based on the obtained traffic and driving information; and controlling the vehicle to operate in accordance with the safety driving model using the one or more changed or updated driving model parameters. A vehicle may seamlessly update operational rules and/or handover of traffic and driving information for transitioning from one region to another.