Disclosed is a method for joint parking, the method including selecting a number of target vehicles participating in the joint parking by a reference vehicle, generating joint parking information based on the selected number of the joint parking vehicles by the reference vehicle, transmitting the generated joint parking information to the target vehicle, and controlling the joint parking to be performed based on the joint parking information.

Disclosed is a system for vehicle control. The system analyzes a state of each controller of multiple controllers inside a vehicle based on information collected from the controllers, learns the state of the each controller, based on a state analysis result obtained by analyzing the state of the each controller, and determines an abnormal state for at least one of the controllers, based on the state analysis result for the each controller and a learning result obtained by learning the state of the each controller. The system also includes a vehicle controller that manages vehicle control rights, based on the abnormal state for the at least one of the one or more controllers.

Disclosed are techniques for improving an advanced driver-assistance system (ADAS) using a secure channel area. In one embodiment, a method is disclosed comprising establishing a secure channel area extending from at least one side of a first vehicle; detecting a presence of a second vehicle in the secure channel area; establishing a secure connection with the second vehicle upon detecting the presence; exchanging messages between the first vehicle and the second vehicle, the messages including a position and speed of a sending vehicle; taking control of a position and speed of the first vehicle based on the contents of the messages; and releasing control of the position and speed of the first vehicle upon detecting that the secure connection was released.

Systems, methods, and other embodiments described herein relate to controlling a trailer without the presence of a physical connection. In one embodiment, a method includes in response to receiving a signal to initiate hitchless maneuvering of a trailer separately from a controlling vehicle, acquiring control inputs to maneuver the trailer from an input device within the controlling vehicle. The method includes communicating, from the controlling vehicle to the trailer, the control inputs to maneuver the trailer. The method includes in response to receiving feedback from the trailer indicating the trailer is within a requested position, sending a control signal to stop the trailer.

A system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance d.sub.x and a longitudinal host vehicle speed v.sub.x that are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase P.sub.t and an traffic light signal timing T.sub.t; obtaining a time of day TOD; providing the host vehicle-traffic light distance d.sub.x, the longitudinal host vehicle speed v.sub.x, the traffic light signal phase P.sub.t, the traffic light signal timing T.sub.t, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.

The present invention obtains a control system, a controller, and a control method capable of appropriately controlling body behavior of plural motorcycles that travel in group.

In a control system (1), a controller (12), and a control method according to the present invention, in plural motorcycles (10) to each of which an environment sensor (11) and the controller (12) are mounted and in each of which a control mode for controlling body behavior is executed by the controller (12) on the basis of output of the environment sensor (11), a first controller that is mounted to a first motorcycle of the plural motorcycles (10) transmits acquired information that is acquired during execution of the control mode to a second controller that is mounted to a second motorcycle other than the first motorcycle of the plural motorcycles (10), and the second controller receives the acquired information and executes the control mode on the basis of the acquired information.

A method for visually tracking a gaze point of a human eye includes: periodically obtaining position coordinates of a human eye of a driver of a host vehicle and coordinates of a gaze point of a sightline of the human eye on ab inner side of a current projection screen; in combination with a refractive index and a curvature of the current projection screen, coordinates of a gaze point of the sightline on an outer side of the current projection screen and a corresponding refracted light path formed by outward refraction of the sightline are obtained, and in combination with a preset normal viewing distance of the human eye, a final gaze point of the sightline on the refracted light path and coordinates of the final gaze point are obtained.

There is provided a method for preventing car collision, the method comprising: obtaining information about a vehicle stopped in front of a preceding vehicle acquired by the preceding vehicle; calculating a deceleration amount for preventing a collision with the stopped vehicle based on the information about the stopped vehicle; predicting a preset event related to the preceding vehicle based on a change in the position of the preceding vehicle; and controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result.

Systems and methods are provided to implement cooperative traffic congestion detection, and enhance the accuracy of detection of traffic congestion for enhanced routing and maneuvering vehicles along a travel route. A vehicle is configured to receive vehicle data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, vehicle movement, traffic, and the like, within the vicinity along the route. The vehicle employs cooperative traffic congestion detection, and fuses data from the plurality of vehicles, including sensor-rich vehicles and legacy vehicles, and applies a learning-based algorithm, such as a machine-learning (ML) algorithm, to generate a real-time and more accurate estimate of traffic congestion.

A sensor assembly can include a housing that includes a view pane and a mounting feature configured to replace a trailer light of a cargo trailer of a semi-trailer truck. The sensor assembly can also include a lighting element mounted within the housing to selectively generate light, and a sensor mounted within the housing and having a field of view through the view pane. The sensor assembly can also include a communication interface configured to transmit sensor data from the sensor to a control system of the self-driving tractor.