A method for safe at least semi-autonomous driving operation of an ego vehicle in case of sun glare is disclosed. The method involves checking, by a computing system, whether one or more vehicle sensors of the ego vehicle are dazzled by sun glare, and if yes, detecting environmental information by a detection system of the ego vehicle. The method further involves subsequently checking, by a computing system, the environmental information for a presence of at least one dynamic object for intercepting the sun glare during driving operation of the ego vehicle, and if yes, checking, by a computing system, whether the ego vehicle can execute a driving manoeuvre in such a way that the at least one dynamic object intercepts the sun glare during driving operation of the ego vehicle. If yes, then the driving manoeuvre is executed.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. One of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. A plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. For each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. The features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. Using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.

This document describes methods and systems for enabling an autonomous vehicle (AV) to determine a path to a stopping location. The AV will determine a desired stop location (DSL) that is associated with a service request. The AV's motion control system will move the AV along a path to the DSL. While moving along the path, the AV's perception system will detect ambient conditions near the DSL. The ambient conditions will be parameters associated with a stopping rule. The AV will apply the stopping rule to the ambient conditions to determine whether the stopping rule permits the AV to stop at the DSL. If the stopping rule permits the AV to stop at the DSL, the motion control system will move the AV to, and stop at, the DSL. Otherwise, the motion control system will not stop the AV at the DSL.

When vehicle forward information includes information on a construction site road sign which indicates the limit speed at a construction site, a vehicle driving assist apparatus acquires the limit speed indicated on the construction site road sign as a construction site sign limit speed, based on the vehicle forward information and displays the acquired construction site sign limit speed on the displaying device. When acquiring the database limit speed while displaying the construction site sign limit speed on the displaying device, and a distance that an own vehicle has moved since acquiring the construction site sign speed limit currently displayed on the displaying device is shorter than a predetermined distance, the vehicle driving assist apparatus continues displaying the construction site sign limit speed on the displaying device without displaying the database limit speed acquired this time on the displaying device.

A process for sensor sharing for an autonomous lane change is provided. The process includes, within a dynamic controller of a host vehicle, monitoring sensors of the host vehicle, establishing communication between the host vehicle and a confederate vehicle on a same roadway as the host vehicle, monitoring sensors of the confederate vehicle, within the dynamic controller of the host vehicle, utilizing data from the sensors of the host vehicle and data from the sensors of the confederate vehicle to initiate a lane change maneuver for the host vehicle, and executing the lane change maneuver for the host vehicle.

A control system for a hybrid vehicle which includes an internal combustion engine and an electric motor and whose drive mode is switchable between an electric vehicle mode and a hybrid vehicle mode includes: an on-board learning unit mounted on the hybrid vehicle and configured to perform a learning action; a position determination unit configured to determine whether the hybrid vehicle is located in a low emission area where operation of the internal combustion engine is supposed to be restricted; and a learning control unit configured to at least partially stop the learning action of the on-board learning unit when determination is made that the hybrid vehicle is located in the low emission area.

Methods and systems are described for vehicle coasting optimization. The system may include a vehicle having an engine, a drivetrain, and an accelerator. The system may include selecting a fuel-saving mode based on an anticipated braking requirement in response to detecting the vehicle is non-stationary and the accelerator is disengaged. The system may include generating an instruction corresponding the selected fuel-saving mode, wherein the instruction is configured to control at least the engine and the drivetrain.

A driving assistance apparatus includes an outside recognition device, a locator unit, and a driving control unit. The outside recognition device is configured to acquire traffic environment information around the vehicle. The locator unit is configured to store road map information and detect a position of the vehicle based on a positioning signal. The driving control unit is configured to control the vehicle based on forward traffic environment information acquired by the outside recognition device. The driving control unit is configured to execute a wrong-way driving detection process and a restricted traffic zone determination process to determine whether the vehicle is performing wrong-way driving in an oncoming lane. The wrong-way driving detection process involves detecting wrong-way driving of the vehicle from the traffic environment information, and the restricted traffic zone determination process involves detecting a restricted traffic zone in a driving lane of the vehicle from the traffic environment information.

Implementations described and claimed herein provide systems and methods for controlling an autonomous vehicle. In one implementation, the autonomous vehicle is navigated towards a flow of traffic with a first gap between first and second vehicles and a second gap following the second vehicle. A motion plan for directing the autonomous vehicle into the flow of traffic at an interaction zone is generated based on whether an ability of the autonomous vehicle to enter the interaction zone at the second gap exceeds a confidence threshold. The autonomous vehicle is autonomously navigated into the flow of traffic at the first gap when the confidence threshold is exceeded. The motion plan forgoes navigation of the autonomous vehicle into the flow of traffic at the first and second gaps when the ability of the autonomous vehicle to enter the interaction zone at the second gap does not exceed the confidence threshold.

According to one embodiment, a motion trajectory boundary is obtained based on a trajectory that has been planned to drive an ADV for a next time period. A safe driving area boundary is determined for the ADV based on perception data perceiving a driving environment surrounding the ADV. The motion trajectory boundary and the safe drivable area boundary are projected onto a map such as an HD map. A relative location of the ADV within the map relative to the motion trajectory and the safe drivable area boundary is determined. A fail-safe action or a fail operational action may be performed based on the relative location of the ADV in view of the motion trajectory boundary and the safe drivable area boundary.