TY - GEN
T1 - Joint Local Reinforcement Learning Agent and Global Drone Cooperation for Collision-Free Lane Change
AU - Hao, Jialin
AU - Naja, Rola
AU - Zeghlache, Djamal
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - This chapter introduces a drone-assisted lane change platform with joint local and global control for collision-less lane change. Specifically, the local control is based on a reinforcement learning agent, DEAR (DEep Q-network with a dynAmic Reward), while the global control is based on drones. The reward function of DEAR is designed from safety, comfort, and efficiency perspectives, and the weights of the three rewards are adjusted according to the surrounding traffic condition. On the other hand, the drones hovering over the highway provide global information (i.e., road vehicular density) to the ego vehicle while performing global control by: (1) computing and sending a dynamic collision reward to the ego vehicle; (2) sending an urgent lane change request (ULCR) to the ego vehicle when a road risk ahead, or an emergency vehicle behind the ego vehicle is detected. The proposed lane change platform is tested with the authentic next-generation simulation (NGSIM) dataset. Simulation results prove that the platform is able to perform safe and efficient lane change on a road prone to risks and emergency vehicles.
AB - This chapter introduces a drone-assisted lane change platform with joint local and global control for collision-less lane change. Specifically, the local control is based on a reinforcement learning agent, DEAR (DEep Q-network with a dynAmic Reward), while the global control is based on drones. The reward function of DEAR is designed from safety, comfort, and efficiency perspectives, and the weights of the three rewards are adjusted according to the surrounding traffic condition. On the other hand, the drones hovering over the highway provide global information (i.e., road vehicular density) to the ego vehicle while performing global control by: (1) computing and sending a dynamic collision reward to the ego vehicle; (2) sending an urgent lane change request (ULCR) to the ego vehicle when a road risk ahead, or an emergency vehicle behind the ego vehicle is detected. The proposed lane change platform is tested with the authentic next-generation simulation (NGSIM) dataset. Simulation results prove that the platform is able to perform safe and efficient lane change on a road prone to risks and emergency vehicles.
KW - Lane change
KW - NGSIM dataset
KW - Reinforcement learning
U2 - 10.1007/978-3-031-34459-6_8
DO - 10.1007/978-3-031-34459-6_8
M3 - Conference contribution
AN - SCOPUS:85174524881
SN - 9783031344589
T3 - EAI/Springer Innovations in Communication and Computing
SP - 99
EP - 114
BT - Future Research Directions in Computational Intelligence - Selected Papers from the 3rd EAI International Conference on Computational Intelligence and Communication
A2 - Hina, Manolo Dulva
A2 - Mirjalili, Seyedali
A2 - Ramdane-Cherif, Amar
A2 - Zitouni, Rafik
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd EAI International Conference on Computational Intelligence and Communications, CICom 2022
Y2 - 4 November 2022 through 5 November 2022
ER -