Abstract: Provided is an intersection deadlock identification method for a mixed flow of autonomous vehicles. This method considers the reality that the intersection traffic flow is composed of human driven vehicles and connected autonomous vehicles. Firstly, the two-dimensional coordinates, speed and front wheel steering angle information of all vehicles in the intersection are obtained, and the blockage graph of vehicles is constructed on the assumption that the front wheel steering angles of all vehicles are fixed. If there is no ring structure in the blockage graph, there is no deadlock; if there is a ring structure, the evasion distance propagation algorithm is used to calculate the evasion requirement distance of a vehicle in the ring. When the evasion requirement distance is greater than the permitted travelling distance of the vehicle itself, a weak traffic deadlock exists.

Abstract: A sensing method and a sensing device for HDVs (human driven vehicles) under a partial VANET environment are provided. According to the method, an existence sensing module and a location sensing module are constructed, based on a long-short-term-memory recurrent neural network, with utilizing historical information of motion states of a single CAV (connected and autonomous vehicle) as an input, existence and exact locations of surrounding HDVs of the CAV are outputted. The method is not only applicable to sensing the surrounding HDVs of the single CAV, but also the surrounding HDVs of the multiple CAVs. An estimation result of each CAV is firstly obtained, then the estimation results of the CAVs are checked with confliction criterion, according to checking results, the estimation results of the multiple CAVs are fused, and information about the surrounding HDVs of each CAV is finally outputted.

Abstract: Provided is an intersection deadlock identification method for a mixed flow of autonomous vehicles. This method considers the reality that the intersection traffic flow is composed of human driven vehicles and connected autonomous vehicles. Firstly, the two-dimensional coordinates, speed and front wheel steering angle information of all vehicles in the intersection are obtained, and the blockage graph of vehicles is constructed on the assumption that the front wheel steering angles of all vehicles are fixed. If there is no ring structure in the blockage graph, there is no deadlock; if there is a ring structure, the evasion distance propagation algorithm is used to calculate the evasion requirement distance of a vehicle in the ring. When the evasion requirement distance is greater than the permitted travelling distance of the vehicle itself, a weak traffic deadlock exists.

Abstract: A dynamic platoon formation method under a mixed autonomous vehicles flow is provided. The method implements dynamic platooning by taking into account a fact that a traffic flow is a mixture of HDVs and CAVs. The dynamic platoon formation method includes: selecting lanes as candidate lanes in turn; constructing a decision tree from a current moment to a moment of platoon formation according to the following process: constructing a decision space for each CAV, generating a compatible decision set, selecting and executing a compatible decision, and updating location and speed information of all vehicles; and selecting, according to a predetermined index (including TTP and DTP), an optimal decision sequence as a decision sequence corresponding to the candidate lane.

Abstract: A dynamic platoon formation method under a mixed autonomous vehicles flow is provided. The method implements dynamic platooning by taking into account a fact that a traffic flow is a mixture of HDVs and CAVs. The dynamic platoon formation method includes: selecting lanes as candidate lanes in turn; constructing a decision tree from a current moment to a moment of platoon formation according to the following process: constructing a decision space for each CAV, generating a compatible decision set, selecting and executing a compatible decision, and updating location and speed information of all vehicles; and selecting, according to a predetermined index (including TTP and DTP), an optimal decision sequence as a decision sequence corresponding to the candidate lane.

Abstract: A sensing method and a sensing device for HDVs (human driven vehicles) under a partial VANET environment are provided. According to the method, an existence sensing module and a location sensing module are constaicted, based on a long-short-term-memory recurrent neural network, with utilizing historical information of motion states of a single CAV (connected and autonomous vehicle) as an input, existence and exact locations of surrounding HDVs of the CAV are outputted. The method is not only applicable to sensing the surrounding HDVs of the single CAV, but also the surrounding HDVs of the multiple CAVs. An estimation result of each CAV is firstly obtained, then the estimation results of the CAVs are checked with confliction criterion, according to checking results, the estimation results of the multiple CAVs are fused, and information about the surrounding HDVs of each CAV is finally outputted.