Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

The present disclosure provides a vehicle travel control method and apparatus. A specific implementation lies in: acquiring a distance between a vehicle and a first intersection, where the first intersection is an intersection for the vehicle to go across on a first road which the vehicle is currently on; acquiring, on a determination that the distance is less than or equal to a preset distance, intersection information of the first intersection and travelling information of the vehicle, where the intersection information includes lane information of at least two lanes on the first road; determining, according to the intersection information and the travelling information, a target weight of each of the lanes for the vehicle to travel into; and determining, according to the target weight of each of the lanes for the vehicle to travel into, an intended travel route of the vehicle.

Evasive steering assist (ESA) systems and methods for a vehicle utilize a set of vehicle perception systems configured to detect an object in a path of the vehicle, a driver interface configured to receive steering input from a driver of the vehicle via a steering system of the vehicle, a set of steering sensors configured to measure a set of steering parameters, and a controller configured to determine a set of driver-specific threshold values for the set of steering parameters, compare the measured set of steering parameters and the set of driver-specific threshold values to determine whether to engage/enable an ESA feature of the vehicle, and in response to engaging/enabling the ESA feature of the vehicle, command the steering system to assist the driver in avoiding a collision with the detected object.

Technologies and techniques for laterally controlling a motor vehicle on a road, where a roadway marking assigned to a driver's side of the motor vehicle is assigned to a lane type based on sensor data. The lane type characterizes whether the roadway marking is assigned to an ego lane on which the motor vehicle is to be guided, or whether it is assigned to a neighboring lane adjacent to the ego lane. The roadway marking is also assigned to the lane type, based on swarm data, and the lane type assigned to the roadway marking is established as the lane type that is determined as a function of the swarm data when the sensor lane type and the swarm data lane type diverge. The motor vehicle is laterally controlled as a function of the established lane type of the roadway marking assigned to the driver's side of the motor vehicle.

A system configured to, and method of, generating and providing a data product using data supplied by a multitude of vehicles that includes receiving a plurality of geographical locations; carrying out a geographical location obfuscation process in order to obtain processed connected vehicle data, wherein the geographical location obfuscation process includes: (i) identifying a road segment based on the received geographical location; (ii) determining an associated road type of the road segment; (iii) determining whether to obfuscate the received geographical location based at least in part on the associated road type; and (iv) when it is determined to obfuscate the received geographical location, obfuscating the received geographical location so as to obtain an obfuscated geographical location, wherein the obfuscated geographical location is included in the processed connected vehicle data; generating the data product using the processed connected vehicle data; and providing the data product to a third party.

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.

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.

A method for improving the ride comfort of a transportation vehicle including planning a first driving route by a navigation system; automatically detecting at least one road parameter of the first driving route by a sensor system of the transportation vehicle; automatically evaluating the first driving route in view of the ride comfort of the first driving route by taking into account the road parameter; and in response thereto using the first driving route or planning an alternative driving route.

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A first error value for a boundary of an object, a second error value for a location of the autonomous vehicle, a third error value for a predicted future location of the object may be received. An uncertainty value for the object may be determined by combining the first error value, the second error value, and the third error value. A lateral gap threshold for the object may be determined based on the uncertainty value. The autonomous vehicle may be controlled in an autonomous driving mode based on the lateral gap threshold for the object.