FuzzyFollow: A Novel Privacy-Aware Intelligent Vehicle-Following Scheme for Safe Driving on Risky Roads Using Fuzzy Sets

Tieming Chen, Xiaoyang Tian, Yinglong Li, Qingyan Jiang, Zechen Liu

Abstract: As an integral component of the Advanced Driver Assistance System (ADAS), intelligent car following plays a vital role in decreasing the accident rate on hazardous roads. Existing car-following methods have issues such as undesired real-time performance and privacy protection. To this end, this paper proposes a privacy-aware fuzzy prediction of the front car braking and fuzzy decision-making of the rear car braking based on dynamic uncertain traffic conditions. Sensitive data, such as location and speed, are transformed into fuzzy information before transmission. This information is then utilized to predict the braking behavior of the front vehicle. Fuzzy rules are developed to facilitate real-time car-following decisions in a lightweight manner. Extensive experimental results show that the overall prediction accuracy of fuzzyFollow reaches 90.4%, outperforming the state-of-the-art work. The proposed scheme outperforms the compared counterparts in terms of communication cost, real-time performance, and privacy protection.