Warning Algorithm of Vehicle Collision Avoidance Based on Driving Intention Sharing in Vehicle-to-vehicle Environment

Aiming at solving the problem of high false alarm rates of traditional early-warning algorithms of vehicle collision avoidance based on distance or time, the concept of driving intent sharing was considered in this study, and a collision avoidance early-warning algorithm based on a complex vehicle-to-vehicle (V2V) environment was proposed. Based on a long term evolution-vehicle(LTE-V) technology, an outfield V2V environment was constructed, and the vehicle driving process was described as a time-series hidden Markov random process. By using a hidden Markov model (HMM), the implicit relationship between a driver's driving intention and the relative driving state sequence of the vehicle was established. The method for predicting the driving intention based on a Viterbi algorithm was also presented. The driving intent was integrated into the safety distance model as a characteristic factor, and a driving intention-based collision avoidance (DI-CA) warning algorithm was proposed. By considering the built V2V experimental environment, four driving intentions of "constant speed," "acceleration," "deceleration," and "emergency braking" were achieved, and the relative speed and relative distance were "increased," "decreased," or "maintained" to obtain experimental data of nine combinations of vehicle driving conditions, such as "change." An empirical analysis of the proposed DI-CA early-warning algorithm based on the experimental data shows that the proposed early-warning algorithm can provide effective vehicle collision early-warning for different driving intentions. Based on the analysis, the safety distances obtained using the DI-CA early warning algorithm and the Mazda early-warning algorithm for four driving intentions were compared and analyzed. The average early-warning accuracy rate of the proposed DI-CA early-warning algorithm is 84%, higher than that of the Mazda early-warning algorithm (78%). The average false alarm rate and missed alarm rate of the DI-CA early-warning algorithm are 5% and 16%, respectively, which are significantly lower than those of the Mazda early-warning algorithm. The results show that the proposed DI-CA early-warning algorithm improves the early-warning effect and significantly reduces the false alarm rate and missed alarm rate, which can minimize the continuous braking caused by false alarms during driving and possible collision accidents caused by missed alarms. Finally, the research prospects of the driving intention sharing theory applied to vehicle collision avoidance warning are highlighted. © 2020, Editorial Department of China Journal of Highway and Transport. All right reserved.