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.

Provided are a system and a method for providing service to recommend customized exercise used for autonomous vehicle according to a situation of a user and a situation of autonomous control by determining that a user does an exercise safely and freely during autonomous control of the vehicle through a monitoring system in the vehicle. According to the present disclosure, it is possible to determine whether the user does the exercise safely and freely during autonomous control of the vehicle through the monitoring system in the autonomous vehicle and recommend the customized exercise used for the autonomous vehicle according to the situation of the user and the situation of the autonomous control. Further, according to the present disclosure, it is possible to determine whether the user does the exercise safely and freely during the autonomous control of the vehicle through the monitoring system in the autonomous vehicle and recommend the autonomous vehicle in which the user can do the recommended exercise according to the situation of the user and the situation of the autonomous control.

The present application provides an intelligent cloud traffic control system, which has a traffic data recalling and transmission architecture arranged hierarchically from bottom to top, and which includes: terminal devices, first-level cloud devices, second-level cloud devices, . . . i.sup.th-level cloud devices; wherein the i.sup.th-level cloud devices include a first cloud device, . . . a j.sup.th cloud device; the i.sup.th-level cloud devices further include: a first cache cloud, . . . a k.sup.th cache cloud arranged corresponding to the j.sup.th cloud device; wherein the j.sup.th cloud device stores a j.sup.th cloud device traffic data set, and the k.sup.th cache cloud stores a k.sup.th characteristic traffic data set; and data in the k.sup.th characteristic traffic data set has a higher priority of being recalled than data in the j.sup.th cloud device traffic data set. Therefore, the present application can preferentially recall traffic data in the cache cloud that has a batter correspondence to task, a smaller range and even a better data structure, thereby significantly improving the data recalling rate and retrieval response efficiency of cloud devices at the same level and even the entire intelligent cloud traffic control system.

Disclosed approaches for selecting a travel route involve considering phase interruptibility of traffic signals on alternative routes. Multiple alternative routes are determined in response to a request for a route. Information describing each route specifies a set of road segments, a set of intersections, and estimated travel times on the road segments. For each road segment that connects to an intersection having a phase-interruptible traffic signal, the estimated travel time on the segment is reduced based on a correction factor associated with the intersection. Estimated travel times on the alternative routes are determined based on the reduced estimated travel times on the segments. One or more of the alternative routes are displayed, including a visual indication of the route having the least estimated travel time.

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.

Properly parking personal mobility vehicles (PMVs) is an important for safety and public satisfaction. The large number of daily rides of ride-share PMVs makes it impossible to verify manually that each PMV is properly parked. Aspects of this disclosure include systems and methods for verifying that PMVs are properly parked. These systems and methods can include training a machine learning model on a server. The system can request that users submit images to the server, such that the server can verify that the user properly parked the PMV. The server can transmit a finished indication when it determines that the user properly parked the vehicle. The server can transmit instructions to the user to take various actions when it determines that the user improperly parked the vehicle or when the image is insufficient to determine whether the user properly parked the vehicle.

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.

A parking detection method, a system, a processing device and a storage medium are provided. The parking detection method includes: acquiring an image to be detected; acquiring a projection area of a vehicle on the ground according to vanishing points and a vanishing line in the image to be detected in combination with outline key points of the vehicle in the image to be detected; judging whether the vehicle is parked regularly.

A method for enriching a map representation of a traffic infrastructure. The method includes: receiving map data of a map representation of a traffic infrastructure, the map representation including pieces of information of a multitude of roadways negotiable by vehicles, the map representation encompassing pieces of information regarding at least one roadway including a transition area; receiving trajectory data of a multitude of first driving trajectories and a multitude of second driving trajectories of a multitude of vehicles; ascertaining a first number of the first driving trajectories and a second number of the second driving trajectories, and comparing the first number to the second number; and marking the first route as a preferred route in the map representation if the first number is greater than the second number.

The present invention relates to a system and a method for correcting position information of a surrounding vehicle, which provide accurate position information of a surrounding vehicle by correcting the position information of the surrounding vehicle received through vehicle-to-vehicle communication, and identifies a license-plate number of a front vehicle through a sensor mounted in a vehicle, calculates a position of the front vehicle, and compare position information, which is included in information including the identified number of the front vehicle in information received from the surrounding vehicle, with the calculated position of the front vehicle to correct the position information of the surrounding vehicle.