String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Abstract

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.