A boarding-alighting position determination method determines a boarding position and an alighting position in a vehicle dispatch system that dispatches a vehicle in response to a vehicle dispatch request. A plurality of boarding position candidates and a plurality of alighting position candidates are calculated around a geographical point from which the vehicle dispatch request was transmitted. A vehicle position is detected when the vehicle dispatch request was transmitted. For each boarding position candidate, a travel route is calculated from the vehicle position at a time of the vehicle dispatch request to at least one of the alighting position candidates via the boarding position candidate. A travel time is calculated for the vehicle to travel the travel route calculated for each of the boarding position candidates. A selected boarding position is determined from among the boarding position candidates based on the traveling time for each boarding position candidate.

Deep reinforcement learning may be used for vehicle repositioning on mobility-on-demand platforms. Information may be obtained. The information may include a current location of a vehicle on a ride-sharing platform. A set of paths originated from the current location of the vehicle may be obtained. Each of the set of paths may have a length less than a preset maximum path length. A set of expected cumulative rewards along the set of paths may be obtained based on a trained deep value-network. A best path from the set of paths may be selected based on a heuristic tree search of the set of expected cumulative rewards. A next step along the best path may be recommended as a reposition action for the vehicle.

The systems and methods described herein disclose detecting events in a vehicular environment using vehicle behavior. As described here, vehicles, either manual or autonomous, that detect an event in the environment will operate to respond to the event. As such, those movements can be used to determine if an event has occurred, even if the event cannot be determined directly. The systems and methods can include collecting detection data about a vehicle behaviors in a vehicular environment. Event behaviors can then be selected from the vehicle behaviors. A predicted event can be formulated based on the event behaviors. The predicted event and an event location can be associated in the vehicular environment. A guidance input can then be formulated for a recipient vehicle. Finally, a recipient vehicle can be navigated using the guidance input.

A local computing device receives lidar data and radar data from one or more road side units (RSUs). The local computing device performs ground plane removal based on range to detect targets and perform local sensor fusion. The local computing device may use a global nearest neighbor (GNN) algorithm and a Kalman filter. The local computing device may create an HD map or the data may be brought together with other target data at a central computing device to produce the HD map. Vehicle position and motion are controlled based on the HD map. Detecting and removing a ground plane based on range are illustrated. Fusion, ground plane removal, and delay filtering may be used in various contexts, such as roadways, parking lots and shipping yards.

According to some aspects of the present disclosure, a method for system-optimal traffic routing is disclosed. In one embodiment, the method includes: obtaining a current state of a traffic network, determining a set of active agents in the traffic network that are configured to receive a system optimal route, where each of the active agents corresponds to a vehicle of a set of vehicles, applying a batching function to the set of vehicles to create a batch of vehicles for replanning; creating sub-batches based on a spatial relationship between the vehicles and temporal relationships between the vehicles, generating a plurality of alternative paths for each of the sub-batches, generating a batch assignment for each of the sub-batches, assigning an alternative path to each agent in the set of active agents based on the batch assignment function, and transmitting the assigned alternative paths to the active agents.

The disclosed technology provides a delivery trailer to be hitched to an autonomous vehicle for autonomous delivery of packages. The delivery trailer can include sensors to provide data to the autonomous vehicle to which it is to overcome limitations caused by any occlusion of sensors on the autonomous vehicle by the trailer. Furthermore, the present technology addresses several challenges related to autonomous package delivery.

A device for controlling travel of a vehicle includes an antenna for receiving a global positioning system (GPS) signal, a processor that identifies the signal received through the antenna to calculate coordinates of a location and a speed of the vehicle, and a display device that provides the calculated result, where the processor stores an estimated time of arrival (ETA) provided when searching for a center route, stores a time of arrival when arriving at a destination, compares the estimated time of arrival with the time of arrival, performs correction on traffic information in consideration of the comparison result, and provides an estimated time of arrival (ETA) specialized for each driver.

Embodiments relate to a system, computer program product, and method for determining shelter areas for two-wheeler vehicles, and, more specifically, for dynamically distinguishing the behavior of two-wheeler vehicles and non-two-wheeler vehicles as an indicator of shelter areas from inclement weather. The behavior of the vehicles is distinguished through a plurality of two-wheeler vehicles slowing down and congregating at a particular location as a shelter against inclement weather, while non-two-wheeler vehicles may slow down, however, not stop proximate this location.

Candidate routes, from an origination to a destination, can be produced. Information can be obtained. The information can be of likelihoods of encounters, along the candidate routes, with types of driving situations demonstrated to cause changes in a degree of confidence, of an occupant of an autonomous vehicle, that a controller of the autonomous vehicle will be able to control the autonomous vehicle so that a result of the encounters is not a collision. Take-over probabilities can be obtained. The take-over probabilities can be of likelihoods that the occupant, in response to the encounters, will cause control of the autonomous vehicle to be transferred to the occupant. Based on the information and take-over probabilities, a route from the origination to the destination can be selected from the candidate routes. The autonomous vehicle can be caused to be configured to commence to move in a direction in accordance with the route.

A control device for a hybrid vehicle includes a driving control part configured to acquire for each driving road section from a server a value of the output parameter linked with a vehicle speed of the same extent as the scheduled vehicle speed when driving over a driving road section on a scheduled driving route to calculate for each driving road section the scheduled vehicle demanded output when driving over the driving road section or acquire from the server the scheduled vehicle demanded output for each driving road section calculated based on the value of the output parameter for each driving road section at the server to heat the catalyst device before using the output of the internal combustion engine as part of the drive power when there is the driving road section where the scheduled vehicle demanded output becomes the engine start output or more.