Inference of HDVs real-time locations in mixed autonomous traffic flow scenario

Abstract

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.

KEYWORDS:

• Bayesian network
• CAV
• car following
• location inference
• VANET

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Project supported by the Key Research and Development Program of China (No. 2019YFB1600300) and the National Natural Science Foundation of China (No. 61873018; 52131202); The ministry of education in china project of humanities and social science (21YJCZH116), Zhejiang province public welfare scientific research project [grant number GF22E088978] and Center for Balance Architecture, Zhejiang University.