A method for controlling a parked vehicle includes receiving vehicle identification information from a first terminal, specifying a target vehicle to be controlled based on the vehicle identification information, waking up the target vehicle to be controlled and transitioning the target vehicle to be controlled to a movable state, and transmitting a moving direction to the target vehicle to be controlled.

A vehicle control device includes a recognizer configured to recognize a surrounding environment of a vehicle, a setter configured to set a first risk area in a surrounding area of the vehicle on the basis of a recognition result of the recognizer, and a controller configured to control at least one of a speed and steering of the vehicle. The setter sets the first risk area so that the first risk area includes an area between the moving object and a first end of a crosswalk where the moving object is scheduled to arrive in the crosswalk when the moving object is entering the crosswalk which is provided in front of the vehicle and where the vehicle is scheduled to pass on the basis of the recognition result of the recognizer. The controller prevents the vehicle from entering the first risk area when a first predetermined condition is satisfied.

Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

A system and a method for autonomous decisioning and operation by an autonomous agent includes: collecting decisioning data including: collecting a first stream of data includes observation data obtained by onboard sensors of the autonomous agent, wherein each of the onboard sensors is physically arranged on the autonomous agent; collecting a second stream of data includes observation data obtained by offboard infrastructure devices, the offboard infrastructure devices being arranged geographically remote from and in an operating environment of the autonomous agent; implementing a decisioning data buffer that includes the first stream of data from the onboard sensors and the second stream of data from the offboard sensors; generating current state data; generating/estimating intent data for each of one or more agents within the operating environment of the autonomous agent; identifying a plurality of candidate behavioral policies; and selecting and executing at least one of the plurality of candidate behavioral policies.

A method helps to protect an occupant of a vehicle (10) equipped with an automated driving system (200) and a vehicle safety system (100) by detecting low impact crash events (99) with the vehicle (10). The method includes utilizing automated driving sensors (220, 230, 240, 250, 260) of the automated driving system (200) to identify possible low impact collision risks. The method also includes utilizing vehicle safety system sensors (110, 115, 120, 125, 130) of the vehicle safety system to determine a low impact collision resulting from the identified possible low impact collision. A vehicle safety system (100) includes an airbag controller unit (150) configured to implement the method to determine low impact crash events with the vehicle (10).

Techniques for determining a speed for a vehicle as it traverses an environment with pedestrians are discussed herein. For example, a vehicle computing system may implement techniques to determine an action for a vehicle to take based on a detected pedestrian in an environment. The vehicle computing system may receive sensor data of an environment from a sensor associated with a vehicle, determine, based at least in part on the sensor data, an object in the environment and receive a predicted object trajectory associated with the object. The vehicle computing system may then determine, based on the predicted object trajectory, a distance between a simulated vehicle passing location and a predicted object location, determine, based on the distance, a speed, determine, based on the distance and the speed, a trajectory for the vehicle to follow, and control the vehicle based on the trajectory.

This document describes Kurtosis based pruning for sensor-fusion systems. Kurtosis based pruning minimizes a total quantity of comparisons performed when fusing together large sets of data. Multiple candidate radar tracks may possibly align with one of multiple candidate visual tracks. For each candidate vision track, a weight or other evidence of matching is assigned to each candidate radar track. An inverse of matching errors between each candidate vision and each candidate radar track contributes to this evidence, which may be normalized to produce, for each candidate vision track, a distribution associated with all candidate radar tracks. A Kurtosis or shape of this distribution is calculated. Based on the Kurtosis values, some candidate radar tracks are selected for matching and other remaining candidate radar tracks are pruned. The Kurtosis aids in determining how many candidates to retain and how many to prune. In this way, Kurtosis based pruning can prevent combinatorial explosions due to large-scale matching.

Techniques for improving the performance of an autonomous vehicle (AV) by automatically annotating objects surrounding the AV are described herein. A system can obtain sensor data from a sensor coupled to the AV and generate an initial object trajectory for an object using the sensor data. Additionally, the system can determine a fixed value for the object size of the object based on the initial object trajectory. Moreover, the system can generate an updated initial object trajectory, wherein the object size corresponds to the fixed value. Furthermore, the system can determine, based on the sensor data and the updated initial object trajectory, a refined object trajectory. Subsequently, the system can generate a multi-dimensional label for the object based on the refined object trajectory. A motion plan for controlling the AV can be generated based on the multi-dimensional label.

An autonomous driving system incorporating human communication into driving related decisions.

The technology involves identifying suitable pickup and drop-off locations based on detected pedestrian walking paths. Mapped areas have specific physical configurations, which may suggest places to pick up or drop off a rider (or a delivery). However, relying solely on map-based information fails to account for how people actually walk or where a most convenient pickup/drop-off spot is located. A walking path heatmap can be generated based on obtained historical and/or real-time pedestrian-related information, which can be obtained by autonomous vehicles driving in areas of interest. Incorporating heatmap information into the evaluation, the system identifies locations for optimized pickup or drop-off in accordance with where pedestrians would likely go. One aspect involves classifying different objects, for instance identifying one or more objects as people who may be walking versus riding a bicycle. Once classified, information about the paths is used to obtain a the heatmap associated with the walking paths.