A first distinct vehicle operational scenario is identified for an autonomous vehicle (AV). A first set of candidate vehicle control actions are received from a model that provides a first solution to the first distinct vehicle operational scenario. An action is selected from the first set of candidate vehicle control actions. The AV is controlled based on the action. The first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.

Aspects of the disclosure relate to determining whether a feature of map information. For example, data identifying an object detected in a vehicle's environment and including location coordinates is received. This information is used to identify a corresponding feature from pre-stored map information based on a map location of the corresponding feature. The corresponding feature is defined as a curve and associated with a tag identifying a type of the corresponding feature. A tolerance constraint is identified based on the tag. The curve is divided into two or more line segments. Each line segment has a first position. The first position of a line segment is changed in order to determine a second position based on the location coordinates and the tolerance constraint. A value is determined based on a comparison of the first position to the second position. This value indicates a likelihood that the corresponding feature has changed.

A vehicle control apparatus includes a microprocessor configured to perform: recognizing a surrounding situation of a subject vehicle; controlling a traveling actuator so that the subject vehicle travels along a target path generated based on a recognition result; and predicting, when an object is recognized in a current lane or in an adjacent lane, whether a passing run is occur based on a relative speed with the object. The generating includes, when the passing run is predicted to occur, generating the target path so that the subject vehicle executes an offset driving in which the subject vehicle is offset in a vehicle width direction relative to the object, and so that an acceleration of the subject vehicle in the vehicle width direction while shifting to or returning from the offset driving is equal to or less than a predetermined value.

In a vehicle traveling remote control system, a remote control apparatus communicates with vehicles and periodically transmits a remote control value used to control traveling of each vehicle. The vehicle traveling remote control system includes a transmission control unit and a remote traveling control unit provided in each vehicle. The transmission control unit transmits to the remote control apparatus information including vehicle outside captured images generated by cameras mounted on the vehicle. The remote traveling control unit periodically executes traveling control of the vehicle based on remote control, with the remote control value. Upon determining that a communication situation between the vehicle and the remote control apparatus or a processing situation of the remote control apparatus influences the traveling control, the transmission control unit selects any of the vehicle outside captured images based on a traveling situation of the vehicle, and transmits the selected image to the remote control apparatus.

Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

A vehicle control device activates a safety device for suppressing a collision between a host vehicle and an oncoming vehicle that is traveling straight along an oncoming traffic lane, when the host vehicle changes from traveling straight along its own traffic lane to making a right or left turn to cross the oncoming traffic lane. The vehicle control device includes a judgement unit and a control unit, where the judgement unit judges when the host vehicle is in a right or left turning condition prior to crossing the oncoming traffic lane, and if is determined that the host vehicle is in the right or left turning condition, the control unit activates the safety device based on a time to lateral collision. The time to lateral collision is obtained by dividing a lateral distance by a lateral velocity, where the lateral velocity is the velocity of the host vehicle in a lateral direction orthogonal to the straight travel direction of the oncoming vehicle, and the lateral distance is the distance, in the lateral direction, from the host vehicle to a predetermined vehicle traffic area defined along the straight travel path of the oncoming vehicle.

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 method ensures that a vehicle can safely pass a traffic light. The vehicle includes a processor and a light sensor. The method includes receiving traffic data, establishing a speed profile, establishing a control distance, the processor establishing, in accordance with the speed profile, a control distance at which braking ensures that the vehicle stops safely before the position of the traffic light, adjustment according to the speed profile, detecting the state of the traffic light when the vehicle is at the control distance from the traffic light, and the processor activating braking if the traffic light is red.

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.