B60W2554/801

METHOD AND SYSTEM FOR SWITCHING BETWEEN LOCAL AND REMOTE GUIDANCE INSTRUCTIONS FOR AUTONOMOUS VEHICLES

Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

ENVIRONMENT-AWARE PATH PLANNING FOR A SELF-DRIVING VEHICLE USING DYNAMIC STEP-SIZE SEARCH
20230131553 · 2023-04-27 ·

A system and method of planning a path for an autonomous vehicle from the vehicle's initial configuration to the goal configuration which is collision-free, kinematically-feasible, and near-minimal in length. The vehicle is equipped with a plurality of perception sensors such as lidar, camera, etc., and is configured to operate in an autonomous mode. An onboard computing device is configured to process the sensor data and provide a dynamic occupancy grid map of the surrounding environment in real-time. Based on the occupancy grid map, the path planner can quickly calculate a collision-free and dynamically feasible path towards the goal configuration for the vehicle to follow.

ENHANCED TARGET DETECTION

Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

Determining the position of a later stopping point of a vehicle
11475765 · 2022-10-18 · ·

Various embodiments include a driver assistance system for determining the position of a stopping point of a vehicle at an infrastructure device comprising: a control unit; a communication device for receiving data from a server or from the infrastructure device; and a sensor arrangement for capturing vehicle data or environmental data. The control unit determines the location of the stopping point at the infrastructure device based at least in part on the data and the vehicle data or environmental data.

Vehicular control system with rear collision mitigation
11472403 · 2022-10-18 · ·

A vehicular control system includes a plurality of sensors disposed at a vehicle and sensing exterior of the vehicle. An electronic control unit (ECU) includes a processor that processes sensor data captured by the sensors. The ECU, responsive at least in part to processing of captured sensor data as the vehicle travels in a traffic lane of a multi-lane road, determines a rearward approaching vehicle rearward of the equipped vehicle that is in an adjacent traffic lane. The ECU determines a leading vehicle ahead of the equipped vehicle and traveling in the same traffic lane as the equipped vehicle. The ECU controls the equipped vehicle to accelerate the vehicle to at least match the speed of the determined rearward approaching vehicle and to maneuver into the adjacent traffic lane to pass the determined leading vehicle ahead of the determined rearward approaching vehicle.

Vehicle control device

A vehicle control device includes: a first detection unit that detects a traveling state of a host vehicle; a merging detection unit that detects that the host vehicle approaches within a predetermined area of a merging point when the host vehicle travels on the merging road toward the merging point at which a main road joins with the merging road; a second detection unit that detects a speed of a lane flow by another vehicle that travels on the main road toward the merging point; a position detection unit that obtains a position of a pre-merging point as a virtual point on the main road reaching the merging point when the host vehicle reaches the merging point; and a display control unit that controls a display device to display the position of the host vehicle and the pre-merging point.

Vehicular sensing system for anticipating cut-in by other vehicle

A method for anticipating a lane change by another vehicle ahead of a vehicle equipped with a sensing system having a camera and a radar sensor includes processing captured image data to determine lane markers of a traffic lane along which the equipped vehicle is traveling, and to determine presence of another vehicle in an adjacent traffic lane. Responsive to processing of captured radar data, an oblique angle of a direction of travel of the other vehicle relative to the traffic lane is determined. Responsive to determination that the oblique angle of the direction of travel of the other vehicle is indicative of a cut-in intent of the other vehicle, and based on the determined range to the determined other vehicle, the system anticipates the cut-in of the other vehicle and applies a braking system of the equipped vehicle to mitigate collision with the determined other vehicle.

Vehicle-trailer distance detection device and method

A method for determining a distance between a camera positioned on a rear portion of a tow vehicle and a trailer coupler supported by a trailer positioned behind the tow vehicle as the tow vehicle approaches the trailer. The method includes identifying the trailer coupler of the trailer within one or more images of a rearward environment of the tow vehicle. The method also includes receiving sensor data from an inertial measurement unit supported by the tow vehicle. The method includes determining a pixel-wise intensity difference between a current received image from the one or more images and a previously received image from the one or more images. The method includes determining the distance based on the identified trailer coupler, the sensor data, and the pixel-wise intensity difference, the distance includes a longitudinal distance, a lateral distance, and a vertical distance.

Method and apparatus related to intra-lane position data indicative of a lateral distance to a lane reference point

In an aspect, a vehicle apparatus of a vehicle obtains, based on sensor data from one or more vehicle sensors communicatively coupled to the vehicle, intra-lane position data relative to the vehicle, the intra-lane position data indicating at least one lateral distance between at least one side of a primary vehicle and at least one lane reference point. The vehicle apparatus transmits the intra-lane position data to one or more neighboring entities. In another aspect, a vehicle management device obtains intra-lane position data that indicates at least one lateral distance between at least one side of at least one observed vehicle of a plurality of neighboring vehicles and at least one lane reference point, and instructs at least one vehicle of the plurality of neighboring vehicles to perform one or more actions based on the intra-lane position data.

Safety system for a vehicle

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.