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.

A method of decelerating a vehicle upon entering a halt zone includes determining a location of a vehicle at routine intervals while the vehicle is traveling along a road; determining when the vehicle enters a halt zone; and upon determining that the vehicle has entered a halt zone, limiting a speed of the vehicle to a nominal speed. The method preferably is performed by an intelligent speed adaptor (ISA) system of a vehicle. The vehicle preferably is part of a fleet of commercial vehicles, and a fleet manager preferably is notified when a vehicle has entered a halt zone.

This application is directed to augmenting training images used for generating vehicle driving models. A computer system obtains a first image of a road, identifies within the first image a drivable area of the road, obtains an image of a traffic safety object, and determines a detour path on the drivable area. The computer system determines positions of a plurality of traffic safety objects to be placed adjacent to the detour path, and generates a second image from the first image by adaptively overlaying a respective copy of the image of the traffic safety object at each of the positions of the plurality of traffic safety objects on the drivable area within the first image. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving a vehicle (e.g., at least partial autonomously).

A method for monitoring uncertainty for human-like behavioral modulation of trajectory planning includes: retrieving map and agent information of a current driving state of an autonomously operated host automobile vehicle; dividing uncertainty conditions affecting a trajectory of the host automobile vehicle into an expected uncertainty and an unexpected uncertainty; calculating the expected uncertainty in a first operation branch by forming attention zones according to identified portions of lanes which may potentially collide with a planned route of the host automobile vehicle; determining the unexpected uncertainty in a second operation branch by calculating an anomaly score for any other vehicles in a surrounding area of the host automobile vehicle positioned in the lanes which may potentially collide with the planned route of the host automobile vehicle; and modulating trajectory operation signals determined for the expected uncertainty if the unexpected uncertainty meets or exceeds a predetermined threshold.

This document describes methods by which a system determines a pickup/drop-off zone (PDZ) to which a vehicle will navigate to perform a ride service request. The system will define a PDZ that is a geometric interval that is within a lane of a road at the requested destination of the ride service request by: (i) accessing map data that includes the geometric interval; (ii) using the vehicle's length and the road's speed limit at the destination to calculate a minimum allowable length for the PDZ; (iii) setting, start point and end point boundaries for the PDZ having an intervening distance that is equal to or greater than the minimum allowable length; and (iv) positioning the PDZ in the lane at or within a threshold distance from the requested destination. The system will then generate a path to guide the vehicle to the PDZ.

A vehicular control system determines a planned path of travel for a vehicle along a traffic lane in which the vehicle is traveling on a road. The system determines a respective target speed for waypoints along the planned path that represents a speed the vehicle should travel when passing through the respective waypoint. The system determines a speed profile for the vehicle to travel at as the vehicle travels along the planned path, with at least two different speeds being based on a difference in target speeds of at least two consecutive respective waypoints of the plurality of waypoints. The system determines an acceleration profile for the vehicle to follow as it changes from one speed to another speed of the speed profile. The system controls the vehicle to maneuver the vehicle along the planned path in accordance with the determined speed and acceleration profiles.

Systems and methods for evaluating vehicle occupant behavior are provided. For example, a method of evaluating vehicle occupant behavior includes analyzing behavior of an occupant of a vehicle associated with a trip in the vehicle. The method also includes determining one or more aspects of the trip in the vehicle. The method also includes determining a score of the behavior of the occupant of the vehicle based on the analysis and the one or more aspects.

System and methods for separating the computation of map feature derivation from the traditional computing pipeline. One or more mapping variables for a sensitive location on a roadway are calculated ahead of time using mapping data derived from previously identified map feature data. When a real time request for processing near the sensitive location is received, the mapping system calculates one or more classification variables for the sensitive location using sensor data included in the request and the previously calculated mapping variables. The classification variables are input into a model configured to output a classification for one or more location features at the sensitive location. The classification is then provided in response to the request.

The present disclosure provides a vehicle control device and method. The vehicle control device includes: a vehicle detection part that detects vehicles existing around the driver's vehicle and outputs a detection result; and a control part coupled to the vehicle detection part. The control part includes: a vehicle control part that controls a vehicle distance to a preceding vehicle based on the detection result; and an adaptive part that uses an adaptation algorithm to accelerate/decelerate the driver's vehicle according to the acceleration/deceleration instruction from the driver when the vehicle control part is executing control of the vehicle distance. The adaptive part changes the vehicle acceleration/deceleration characteristic of the driver's vehicle in the control of the vehicle distance based on the history of the acceleration/deceleration instruction.

Systems and methods for identifying objects in an environment of a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive images representative of the environment of the host vehicle; assign first pixel descriptor values to a plurality of pixels associated with a first image and second pixel descriptor values to a plurality of pixels associated with a second image; identify object representations in the first image and the second image based on at the first pixel descriptor values and the second pixel descriptor values, respectively; determine a first object descriptor and a second object descriptor based on the first pixel descriptor values and the second pixel descriptor values, respectively; and based on a comparison of the first object descriptor and the second object descriptor, output an indication that the object representations in the first image and the second image represent a common object.