An error monitoring apparatus and method are provided. The error monitoring method includes determining whether or not a predetermined event has occurred to a first mobility; sensing to collect an event-related potential (ERP) for at least one passenger of the first mobility for a predetermined amount of time, analyzing the collected ERP based on the determination, and transmitting error information of the first mobility to a traffic control server based on an analysis result. Herein, the predetermined event includes a traffic accident related at least to the first mobility, and the error information of the first mobility includes at least one of time information regarding when the ERP occurs, a waveform of the ERP, location information of the first mobility, or operational information of the first mobility.

A managing apparatus provides self-driving of a vehicle, which allows the vehicle to enter a parking space out of a plurality of parking spaces from an outside of a parking lot including the plurality of parking spaces, and which allows the vehicle to leave the parking lot for a predetermined place in the outside. The managing apparatus is provided with: a wait time setting device configured to set a maximum wait time of the vehicle in the predetermined place; and a controller configured to set a driving route for returning the vehicle to the parking lot from the predetermined place on condition that the maximum wait time elapses from an arrival of the vehicle in the predetermined place, and configured to provide the self-driving of the vehicle along the driving route.

A vehicle control device includes an external situation recognition unit configured to recognize a crossing person who crosses over a path of a vehicle and acquire information on the crossing person and information on an environment where the crossing person crosses, a scheduled departure time deciding unit configured to decide a scheduled departure time of the vehicle based on the information on the crossing person and the environment where the crossing person crosses when the crossing person is recognized by the external situation recognition unit, and an informing controller configured to perform a control to inform an outside of the vehicle of the scheduled departure time. The scheduled departure time deciding unit predicts a crossing completion time at which the crossing person recognized by the external situation recognition unit completes the crossing and decides the scheduled departure time based on the crossing completion time.

The present disclosure provides a vehicle control system capable of safely and forcibly controlling a vehicle. A vehicle control system VCS includes a mode setting unit 56 configured to output a forced mode signal upon receiving a forced control instruction from the outside of a vehicle V via a communication device 30, and a forced operation unit 58 configured to output a forced operation signal of forcibly operating the vehicle V to an operation control unit 20. Further, the vehicle control system VCS includes a signal selection unit 59 configured to allow all the operation signals to pass therethrough before receiving the forced mode signal, and block at least a part of the operation signals and allow the forced operation signal instead of the blocked operation signal to pass therethrough after receiving the forced mode signal. The vehicle control system VCS controls an operation of the vehicle V by controlling the operation control unit 20 based on the operation signal or the forced operation signal passing through the signal selection unit 59.

A method of estimating an accident risk level of a first traffic participant based on interactions or negotiations of the first traffic participant with one or more other traffic participants is provided. The method includes generating a plurality of virtual trajectories of the first traffic participant based on a recorded initial position, a recorded final position of the first traffic participant, and a recorded initial position of each of the one or more other traffic participants. The plurality of virtual trajectories of the first traffic participant are associated with a plurality of virtual behaviors of the first traffic participant. The method further includes identifying a virtual trajectory that is most similar to a recorded trajectory of the first traffic participant. The method enables an automatic interpretation of an actual maneuver of the first traffic participant based on the virtual behavior of first traffic participant associated with the identified virtual trajectory.

Systems and methods for real-time detection and mitigation anomalous behavior of a remote vehicle are provided, e.g., vehicle behavior that is consistent with distracted or unexpectedly disabled driving. On-board and off-board sensors associated with a subject vehicle may monitor the subject vehicle's environment, and behavior characteristics of a remote vehicle operating within the subject vehicle's environment may be determined based upon collected sensor data. The remote vehicle's behavior characteristics may be utilized to detect or determine the presence of anomalous behavior, which may be anomalous for the current contextual conditions of the vehicles' environment. Mitigating actions for detected remote vehicle anomalous behaviors may be suggested and/or automatically implemented at the subject vehicle and/or at proximate vehicles to avoid or reduce the risk of accidents, injury, or death resulting from the anomalous behavior. In some situations, authorities may be notified.

Systems and methods are provided for obtaining vehicle operating conditions at or near a section of roadway in order to determine whether existing/current roadway infrastructure at or near that section of roadway is causing a loss in operating efficiency. Vehicle operating conditions may be communicated by vehicles to roadside units via a vehicle-to-infrastructure communications system. Upon a determination that the existing/current roadway infrastructure is causing the loss in operating efficiency, one or more recommendations for improvements to the existing/current roadway infrastructure may be suggested to a municipality controlling the section of roadway. Estimates regarding the effectiveness of the improvements as well as actual measurements regarding implemented improvements may be determined in order to justify proceeding with implementing the improvements and/or generate additional improvement recommendations.

An information processing system includes a vehicle and a server that is communicable with the vehicle. The vehicle acquires a moving image obtained by imaging an oncoming lane during traveling. At least one of a congestion section and a congestion degree of the oncoming lane is determined based on the moving image. The server stores at least one of the congestion section and the congestion degree of the oncoming lane and provides information to a client by using the stored information.

A method for generating the at least one highway exit indicator, the method may include receiving video information and location information obtained during driving sessions of the plurality of vehicles; determining, based on the location information, multiple suspected highway exit events; selecting video information segments, wherein each selected video information segment is acquired before a suspected highway exit event and in timing proximity to the suspected highway exit event; and applying a machine learning process on at least some of the selected video information segments to find the at least one highway exit indicator.

Systems and methods for determining autonomous vehicle user boarding times are provided. In one example embodiment, a computer implemented method includes obtaining location data associated with a user device associated with a user. The method includes determining an estimated time until the user starts boarding the autonomous vehicle based at least in part on the location data associated with the user device. The method includes obtaining data associated with the user. The method includes determining an estimated time of boarding duration for the user based at least in part on the data associated with the user. The method includes determining an estimated time until the user completes boarding of the autonomous vehicle based at least in part on the estimated time until the user starts boarding the autonomous vehicle and the estimated time of boarding duration for the user.