Deep Q‐network based multi‐layer safety lane changing strategy for vehicle platoon

Abstract

Abstract The vehicle platoon lane changing is significant for alleviating road congestion and diminishing transportation energy consumption. However, the lane changing strategy for a group of vehicles is still a great challenge in this field. This paper investigates the vehicle platoon lane changing problems, in which the safety and efficiency in the lane changing procedure are both taken into consideration. Since the safety of the platoon lane changing would be affected by the lane changing gap and the length of the platoon, a novel platoon lane changing strategy is proposed by using the deep Q‐network. In detail, the proposed platoon lane changing strategy contains two layers, where the first one is a decision layer and the other one is the verification layer. In the decision layer, the deep Q‐network is employed to improve the lane changing efficiency. Then, the verification layer is presented to enhance the platoon lane changing safety. In final, some typical platoon lane changing scenarios are provided in an existing ramp containing a vehicle platoon and some random vehicles. The related numerical simulations are conducted to validate the feasibility and effectiveness of the proposed approaches.

Keywords

• decision making
• learning (artificial intelligence)
• intelligent transportation systems
• vehicle routing