no code implementations • 19 Jan 2024 • Xuanlei Zhao, Shenggan Cheng, Guangyang Lu, Jiarui Fang, Haotian Zhou, Bin Jia, Ziming Liu, Yang You
The experiments demonstrate that AutoChunk can reduce over 80\% of activation memory while maintaining speed loss within 10%, extend max sequence length by 3. 2x to 11. 7x, and outperform state-of-the-art methods by a large margin.
no code implementations • 26 Dec 2023 • Yuqi Zheng, Ruidong Yan, Bin Jia, Rui Jiang, Adriana TAPUS, Xiaojing Chen, Shiteng Zheng, Ying Shang
In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and significantly improve the performance of car following.
no code implementations • 12 Dec 2022 • Hui Wang, Jialin Liu, Feng Li, Hao Ji, Bin Jia, Ziyou Gao
Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) when occurring a disruption event during peak hours, the impact on the normal timetable is greater, and passengers in the direction with fewer train services are more affected; (2) if passengers stranded at the terminal stations of disruption area are not transported in time, they will rapidly increase at a speed of more than 300 passengers per minute; (3) compared with the fixed shortest path, using the response vehicles reduces the total travel time about 7%.
no code implementations • 24 Feb 2021 • Ruidong Yan, Rui Jiang, Bin Jia, Jin Huang, Diange Yang
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments.