Nissan North America, Inc.

University of Massachusetts

Renault S.a.s.

Viju James

Najamuddin Mirza Baig

Liam Pedersen

Fang-Chieh Chou

Simon Tien

Abdul Rahman Kreidieh

Stefan Witwicki

Christopher Ostafew

Shlomo Zilberstein

Siddharth Thakur

Armelle Guerin

Astha Vagadia

Katsuhiko Hirayama

Mark Fischer

A control signal related to a road segment is received by a processor of a vehicle from a remote server. Using at least a portion of the control signal, a cruise control parameter for controlling the vehicle is obtained. Responsive to determining that a cruise control module of the vehicle is enabled, the cruise control module is configured to use the cruise control parameter. The cruise control module operates to maintain the cruise control parameter for the vehicle.

Congestion management with cruise control

A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.

Generation and transmission of control commands for connected vehicles based on predicted future flow, average velocity, or future density of traffic

Intersection collision avoidance may use a cellular transceiver for a cellular network and a processor located at a cellular access point for multi-access edge computing. The processor includes a shared world model and a conflict detection module. The shared world model is configured to receive, by the cellular transceiver, signals relating to at least two road users in proximity to an intersection within a vehicle transportation network, wherein the at least two road users include an ego vehicle, and the signals conform to a standards-based communication protocol. The conflict detection module is configured to receive object information from the shared world model, determine a potential future collision between the ego vehicle and an other road user of the at least two road users, and transmit a notification of the potential future collision to the ego vehicle over the cellular network.

System and method for intersection collision avoidance

A curb management system for a host vehicle includes a sensor, an electronic controller, and a wireless communication system. The sensor is configured to detect information relating to a presence of a parked vehicle along a curb of a road. The electronic controller is configured to determine curb management information based on the information detected by the sensor. The wireless communication system is configured to transmit the curb management information to a remote server and to receive a predicted curb availability from the remote server.

System and method of curb management for a vehicle

An intersection collision avoidance system determines, for an ego vehicle, a direction indicated by its turn signal, its destination setting, or both, generates, where the direction is determined, a possible intended path relative to an intersection using a high-definition (HD) map and the direction, and generates, where the destination setting is determined, a possible intended path for using the HD map and the destination setting. Where the direction and the destination setting are both determined, the direction indicated by the turn signal is compared to the possible intended path generated using the destination setting, and one of the possible intended paths is selected based on the comparison. The system transmits, to a conflict detection module, a set of drive goals for the ego vehicle relative to the intersection that conforms to the intended path. The module can determine a possible collision with another road user using the drive goals.

Resolving an exception situation in autonomous driving includes receiving an assistance request to resolve the exception situation from an autonomous vehicle (AV); identifying a solution to the exception situation; forwarding the solution to a tele-operator; receiving a request for playback data from the tele-operator; receiving, from the AV, the playback data; and obtaining, from the tele-operator, a validated solution based on the tele-operator using the playback data. The playback data includes snapshots nof data related to autonomous driving stored at the AV at respective consecutive times t, for i=1, . . . , N.

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Fremont, CA, United States of America

Viju James