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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
ترجمه فارسی عنوان مقاله:
کنترل سریع ، کارآمد و ایمن بر اساس یادگیری تقویتی برای رانندگی خودمختار
منبع:
Sciencedirect - Elsevier - Transportation Research Part C, 117 (2020) 102662. doi:10.1016/j.trc.2020.102662
نویسنده:
Meixin Zhua,b, Yinhai Wangb,⁎, Ziyuan Pub, Jingyun Hub, Xuesong Wanga,c,⁎, Ruimin Keb
چکیده انگلیسی:
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by referencing
human driving data and combining driving features related to safety, efficiency, and comfort.
With the developed reward function, the RL agent learns to control vehicle speed in a fashion that
maximizes cumulative rewards, through trials and errors in the simulation environment. To avoid
potential unsafe actions, the proposed RL model is incorporated with a collision avoidance
strategy for safety checks. The safety check strategy is used during both model training and
testing phases, which results in faster convergence and zero collisions. A total of 1,341 carfollowing
events extracted from the Next Generation Simulation (NGSIM) dataset are used to
train and test the proposed model. The performance of the proposed model is evaluated by the
comparison with empirical NGSIM data and with adaptive cruise control (ACC) algorithm implemented
through model predictive control (MPC). The experimental results show that the
proposed model demonstrates the capability of safe, efficient, and comfortable velocity control
and outperforms human drivers in that it 1) has larger TTC values than those of human drivers, 2)
can maintain efficient and safe headways around 1.2s, and 3) can follow the lead vehicle comfortably
with smooth acceleration (jerk value is only a third of that of human drivers). Compared
with the MPC-based ACC algorithm, the proposed model has better performance in terms of
safety, comfort, and especially running speed during testing (more than 200 times faster). The
results indicate
Keywords: Car following | Autonomous driving | Velocity control | Reinforcement learning | NGSIM | Deep Deterministic Policy Gradient (DDPG)
قیمت: رایگان
توضیحات اضافی:
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