Design and Evaluation of a Human-in-the-Loop Connected Cruise Control

Abstract

The performance of automated vehicle can be greatly improved by enabling vehicle-to-vehicle communication. An example of such applications is Cooperative Adaptive Cruise Control (CACC) along the highway. Although the adoption rate of vehicular connectivity is predicted to grow rapidly, CACC can only benefit vehicles that are both connected and automated. To take a full advantage of vehicular connectivity, a human-in-the-loop Connected Cruise Control (hCCC) algorithm is developed for human-driven connected vehicle. In hCCC, the human driver remains engaged in the longitudinal control of the vehicle, and hCCC controller applies additional acceleration/deceleration on top of human actions according to the received status of preceding vehicle. By allowing coexistence of the automatic control and driver's actions in a beneficial way, hCCC helps the human driver stabilize the vehicle more efficiently and safely. The proposed hCCC inherits the feedback-feedforward control structure and velocity-dependent spacing policy from the typical CACC systems. String stability analysis shows that hCCC can offer broader string-stable ranges of human parameters than human driving alone or the existing acceleration-based Connected Cruise Control (CCC), indicating a better capability to mitigate traffic disturbance with the uncertain human behaviors. The desirable properties of hCCC were validated in driving simulator experiments, which showed that hCCC could reduce 36.8% acceleration, 31.2% time-gap fluctuation, 81.2% exposure time to unsafe driving situations, and 15.8% fuel consumption from those of human driving alone. In addition, two derivative designs of hCCC are proposed and proven effective, further lowering down the practice threshold of hCCC.