A Human-Like Model to Understand Surrounding Vehicles’ Lane Changing Intentions for Autonomous Driving

Abstract

Autonomous vehicles have to share the road with human-driven ones for a prolonged period in the near future. Thus, they should have the ability to understand the lane changing intentions of surrounding human-driven vehicles. Unlike human drivers, the state-of-the-art models used in autonomous vehicles recognize a driver's lane changing intention based on the target vehicle's lateral movement, which leaves little to no time for the autonomous vehicle to react. In this paper, a Human-like Lane Changing Intention Understanding Model (HLCIUM) for autonomous driving is proposed to understand the lane changing intentions of surrounding vehicles. By imitating the selective attention mechanism of human vision systems, the proposed model emulates the way human drivers concentrate on the surrounding vehicles and recognizes their lane changing intentions accordingly. The velocity changes of the surrounding vehicles are treated as the lane changing hints, and the attention is drawn to the corresponding vehicle following a saliency-based scheme. Then, the lane changing intention is identified by a Hidden Markov Model (HMM) based intention recognizer. The proposed model is tested with Next Generation Simulation (NGSIM) vehicle trajectory dataset, and the proposed method has reached 90.89% in detecting lane changing intention and 88.58% in lane keeping in urban road scenarios, and reached 87.73% and 87.48% for the lane changing and lane keeping intentions in highway scenarios, respectively. Importantly, the average recognition time before the lane changing maneuver of the proposed model is 6.67 seconds for the urban road datasets and 7.08 seconds for the highway datasets, which is far earlier than state-of-the-art models. Furthermore, the proposed method shows efficiency and robustness in complex real urban traffic datasets, which is ideal to use in human-like autonomous driving systems.