Methods and systems for communicating between autonomous vehicles are described herein. Such communication may be performed for signaling, collision avoidance, path coordination, and/or autonomous control. A first autonomous vehicle may receive a communication from a second autonomous vehicle travelling on the same road as the first autonomous vehicle, where the communication includes an indication of a maneuver which will be performed by the second autonomous vehicle. The first autonomous vehicle may then analyze the communication to identify a first maneuver for the first autonomous vehicle in response to the second maneuver performed by the second autonomous vehicle. Thus, the first autonomous vehicle may move in accordance with the first maneuver.

The technology involves pickups of riders by autonomous vehicles in a manner that ensures the rider is picked up within an estimated time of arrival (ETA). For instance, in accordance with customer authorization, the autonomous vehicle may loiter or otherwise stay within a certain proximity (e.g., distance or time) to guarantee rider pickup within a predetermined time. One vehicle may be assigned to a rider for a set timeframe or multiple vehicles may be allocated to a particular event. Either approach may be used to ensure rider pickup with minimal waiting. One benefit is to avoid user-initiated ride requests when the customer is ready to depart a location, because a vehicle will already be present and ready to take the rider to their desired destination. Loitering may include prepositioning a vehicle at a given place, or driving autonomously to be nearby as needed.

Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.

A driving assistance device of an embodiment includes a recognizer that recognizes a state of a surrounding object that is an object present in the surroundings of a vehicle and a traveling status of the vehicle, a detector that detects a first direction that is a direction of a line of sight of a driver of the vehicle, a determiner that determines whether or not the surrounding object is a target object to be watched by the driver on the basis of the state of the surrounding object and the traveling status, and a display controller that, in a case where it is determined that the surrounding object is the target object, displays first information for guiding the line of sight of the driver toward the target object on one or a plurality of displays on the basis of the first direction detected by the detector and a second direction in which the target object is present when viewed from the driver, in which the display controller determines at least one display to display the first information from among the plurality of displays on the basis of the first direction detected by the detector, or determines a mode of the first information to be displayed on the display.

A vehicle control device configured to control a self-vehicle, the vehicle control device comprising: a first detection unit configured to detect a direction change of an oncoming vehicle; a second detection unit configured to detect another vehicle on a diagonally rear side of the self-vehicle; and a control unit configured to control a notification unit on the basis of detection results of the first detection unit and the second detection unit, wherein the control unit controls the notification unit to notify the oncoming vehicle of presence of the other vehicle in a case where the direction change of the oncoming vehicle has been detected and the other vehicle has been detected.

A method and apparatus for controlling a first vehicle autonomously are disclosed. For instance, one or more processors may plan to maneuver the first vehicle to complete an action and predict that a second vehicle will take a particular responsive action. The first vehicle is then maneuvered towards completing the action in a way that would allow the first vehicle to cancel completing the action without causing a collision between the first vehicle and the second vehicle, and in order to indicate to the second vehicle or a driver of the second vehicle that the first vehicle is attempting to complete the action. Thereafter, when the first vehicle is determined to be able to take the action, the action is completed by controlling the first vehicle autonomously according to whether the second vehicle begins to take the particular responsive action.

At least one processor of a vehicle is configured to execute at least one program to: generate a speed plan for a first travel route from a blind spot elimination position to a position specified by a control standby condition, the speed plan specifying a speed of the vehicle for a position on the first travel route so as to meet a requirement that the vehicle be decelerated at a predetermined allowable deceleration or less for a predetermined time from the blind spot elimination position to satisfy the control standby condition; and instruct, for a second travel route from a current position of the vehicle to the blind spot elimination position, the vehicle to travel along the second travel route by autonomous driving so as to cause the vehicle to reach a speed specified by the speed plan at the blind spot elimination position.

Systems and methods for training AV models in accordance with embodiments of the invention are illustrated. One embodiment includes an autonomous vehicle (AV), a vehicle, a processor, and a memory, where the memory contains an AV model capable of driving the vehicle without human input, where the AV model is trained on a plurality of edge case scenarios. In a still further additional embodiment, a method for training AV models, including obtaining a data structure storing a plurality of scenarios that an AV can encounter, and distance metrics indicating the distance between each scenario, generating a list of edge case scenarios within the plurality of scenarios, identifying hazard frames within the edge case scenarios, encoding the hazard frames into one or more records interpretable by an AV model, and training the AV model using the one or more records.

The present disclosure relates to a method and system for communication between a vulnerable road user and an autonomous vehicle using augmented reality to highlight information to the vulnerable road user regarding potential interactions between the autonomous vehicle and the vulnerable road user.

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera, at least one captured image representative of features in an environment of the vehicle. The processor may identify an intersection and a pedestrian in a vicinity of the intersection represented in the image. The processor may determine a navigational action for the vehicle relative to the intersection based on routing information for the vehicle; and determine a predicted path for the vehicle relative to the intersection based on the determined navigational action and a predicted path for the pedestrian based on analysis of the image. The processor may further determine whether the vehicle is projected to collide with the pedestrian based on the projected paths; and, in response, cause a system associated with the vehicle to implement a collision mitigation action.