期刊
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
卷 10, 期 1, 页码 468-498出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/21680566.2021.2007815
关键词
Bayesian network; CAV; car following; location inference; VANET
资金
- Key Research and Development Program of China [2019YFB1600300]
- National Natural Science Foundation of China [61873018, 52131202]
- ministry of education in china project of humanities and social science [21YJCZH116]
- Zhejiang province public welfare scientific research project [GF22E088978]
- Center for Balance Architecture, Zhejiang University
The study proposed a method using CAVs' sensing data to infer information about HDVs, addressing the issue of monitoring HDVs in mixed autonomous traffic flow, and successfully tested it with real-world dataset.
In the near future, the road traffic flow will consist of both human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Since HDVs cannot communicate with CAVs and road side units (RSU), they are unobservable to CAVs if outside the range of sensing. In such case, the advantages of CAVs will be compromised and various high-level tasks in mixed autonomous traffic flow cannot be achieved. This study proposes a model to infer HDVs information using sensing data of CAVs. The rationale is that CAVs react to HDVs based on the car-following (CF) logic. Inversely, real-time locations of HDVs can be reconstructed using the data from CAVs. The Bayesian network is used to reflect the CF logic and develop a real-time vehicle location inference method for both single and multiple CAVs scenarios. Last, the method is tested using real-world dataset. Both time consumption and near-field estimation precision are validated.
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