Enhanced Adaptive Cruise Control Strategy for Improving Driving Safety in Connected Vehicle Environment

In the connected vehicle (CV) conditions, the driver behaviors of self-driving or semi-automatic vehicles as well as the vehicle motion in highly interactive traffic scenarios can be realized, which in turn, enhance the proactive actions of adaptive cruise control (ACC) in emergency cases. In this paper, the uncertainty of the vehicle motion model AND the random effect of traffic flow in the connected vehicle conditions are considered to improve the modelling process of the ACC system. Firstly, we apply the CV technology to construct a novel ACC framework, which comprehensively monitor the vehicle motion and driving behaviors for both preceding and following vehicles in the string. Then, the proposed CV-ACC strategy has been applied to identify the vehicle motion pattern, i.e. constant speed, acceleration/decelerations, in car following scenarios, track the trajectory of the preceding vehicle. Finally, a model predictive control (MPC) method is executed to generate an optimal manipulation strategy for the following vehicle in order to keep minimum safety headway and harmonious speed change in accordance to the preceding vehicle. Compared with traditional ACC, the response time of CV-ACC can be advanced by about 0.5s, and the car following can be more stable and safer. By taking advantage of connected vehicle based ACC, the preceding vehicle's motion and driver behaviors can be real-time detected and integrated for developing an effective ACC strategy, which provides an applicable way to the realization of large-scale fleet collision avoidance in mix traffic situations