scholarly journals Enhancing Mixed Traffic Flow Safety via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanqiu Cheng ◽  
Chenxi Chen ◽  
Xianbiao Hu ◽  
Kuanmin Chen ◽  
Qing Tang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Trajectory Planning ◽  
Autonomous Vehicle ◽  
Planning Problem ◽  
Mixed Traffic ◽  
Benchmark Model ◽  
Mixed Traffic Flow ◽  
Tree Expansion ◽  
Study Results

The longitudinal trajectory planning of connected and autonomous vehicle (CAV) has been widely studied in the literature to reduce travel time or fuel consumptions. The safety impact of CAV trajectory planning to the mixed traffic flow with both CAV and human-driven vehicle (HDV), however, is not well understood yet. This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. Crash potential index (CPI) is defined to quantitively measure the safety performance of the mixed traffic flow. The CAV trajectory planning problem is firstly formulated as an optimization model; then, the solution procedure based on reinforcement learning is proposed. The tree-expansion determination module and rollout termination module are developed to identify and reduce the unnecessary tree expansion, so as to train the model more efficiently towards the desired direction. The case study results showed that the proposed algorithm was able to reduce the CPI by 76.56%, when compared with a benchmark model without any intelligence, and 12.08%, when compared with another benchmark model that the team developed earlier. These results demonstrated the satisfactory performance of the proposed algorithm in enhancing the safety of the mixed traffic flow.

scholarly journals Trip Cost Estimation of Connected Autonomous Vehicle Mixed Traffic Flow in a Two-Route Traffic Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhizhen Liu ◽  
Hong Chen ◽  
Hengrui Chen ◽  
Xiaoke Sun ◽  
Qi Zhang
Keyword(s):  
Bounded Rationality ◽  
Traffic Flow ◽  
Cost Estimation ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Flow Simulation ◽  
Mixed Traffic ◽  
Traffic Condition ◽  
Mixed Traffic Flow ◽  
Traffic Flow Simulation

With the advancement of connected autonomous vehicle (CAV) technology, research on future traffic conditions after the popularization of CAVs needs to be resolved urgently. Bounded rationality of human drivers is essential for simulating traffic flow precisely, but few studies focus on the traffic flow simulation considered bounded rationality in CAV mixed traffic flow. In this study, we introduce random bounded rationality into the hybrid feedback strategy (HFS) under CAV mixed traffic flow to explore the impacts of CAV penetration rate on the trip cost of vehicles. First, we investigated the bounded rationality of drivers, and we found that it follows normal contribution. Then, we proposed HFS considering random bounded rationality and the CAV penetration rate to simulate the traffic condition. The numerical results show that the enhancement of the CAV penetration rate could reduce total trip cost. The research could help us to simulate the CAVs mixed traffic flow more precisely and realistically.

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

2020 ◽  
Vol 2674 (8) ◽  
pp. 167-178
Author(s):  
Yanqiu Cheng ◽  
Xianbiao Hu ◽  
Qing Tang ◽  
Hongsheng Qi ◽  
Hong Yang
Keyword(s):  
Monte Carlo ◽  
Real Time ◽  
Traffic Flow ◽  
Tree Search ◽  
Mixed Traffic ◽  
Solution Quality ◽  
Monte Carlo Tree Search ◽  
Mixed Traffic Flow ◽  
Model Free ◽  
Tree Expansion

A model-free approach is presented, based on the Monte Carlo tree search (MCTS) algorithm, for the control of mixed traffic flow of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV), named MCTS-MTF, on a one-lane roadway with signalized intersection control. Previous research has often simplified the problem with certain assumptions to reduce computational burden, such as dividing a vehicle trajectory into several segments with constant speed or linear acceleration/deceleration, which was rather unrealistic. This study departs from the existing research in that minimum constraints on CAV trajectory control were required, as long as the basic rules such as safety considerations and vehicular performance limits were followed. Modeling efforts were made to improve the algorithm solution quality and the run time efficiency over the naïve MCTS algorithm. This was achieved by an exploration-exploitation balance calibration module, and a tree expansion determination module to expand the tree more effectively along the desired direction. Results of a case study found that the proposed algorithm was able to achieve a travel time saving of 3.5% and a fuel consumption saving of 6.5%. It was also demonstrated to run at eight times the speed of a naïve MCTS model, suggesting a promising potential for real-time or near real-time applications.

Study on urban three-lane mixed traffic flow with buses based on the Nagel-Schreckenberg model

2013 ◽  
Vol 6 (6) ◽  
pp. 615
Author(s):  
Changxi Ma ◽  
Fang Wu ◽  
Bo Qi ◽  
Liang Gong ◽  
Li Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Mixed Traffic ◽  
Mixed Traffic Flow

scholarly journals Properties of vehicle gap distribution in the mixed traffic flow

2012 ◽  
Vol 31 ◽  
pp. 1001-1005 ◽  
Author(s):  
Qilang Li ◽  
Binghong Wang
Keyword(s):  
Traffic Flow ◽  
Mixed Traffic ◽  
Mixed Traffic Flow
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