Modified Cooperative Adaptive Cruise Control Strategy for Optimizing Cooperative Driving in Freeway Weaving Segments

Abstract

This paper comprehensively investigates the feasibility of a modified cooperative adaptive cruise control (CACC) strategy to support car platooning in freeway weaving segments. Firstly, to improve the system response to cut-out and cut-in disturbance, the control structure of the typical original CACC system was modified and a synthesis control structure was designed that integrates the feedforward control module to the conventional feedback CACC system. Based on the modified control structure, a variable acceleration limited (VAL) control algorithm is proposed, which is specifically applied in the feedforward control model to suppress the propagation of the cut-in and cut-out disturbances in the CACC platoon. To investigate the performance of the VAL-CACC strategy, a series of comparative simulation experiments were carried out under simulated freeway weaving areas. In these tested scenarios, the occurance of frequent lane changing is considered, and the cut-out and cut-in disturbance of the CACC platoon are investigated, respectively. The obtained experimental results revealed that, compared with the original CACC system, the proposed VAL-CACC strategy can ameliorate the acceleration fluctuation amplitude, reducing it by 12.5% in the tested cut-out scenario. On the other hand, in the cut-in comparative experiments, it is found out that the VAL-CACC strategy can more significantly reduce by about 48.33% the jitter amplitude caused by the merging-in behavior of the non-platooned vehicle. These results demonstrate that the proposed synthesis VAL-CACC strategy can dramatically improve the CACC platoon’s ability to resist cut-in and cut-out disturbances.

Pandemic effects to autonomous vehicles test operations in California

“…Recent studies in automated driving systems have addressed more complicated road situations such as disturbances to the traffic flow caused by weaving, deviating, cutting-in and cutting-out, lane changing, and ramp exiting driving behaviors, and includes the development of optimal lane change strategies for automated vehicles by predicting the impacts of lane changes [ 59 – 61 ]. Recent studies include research into more efficient transportation techniques such as cooperative driving in platoons [ 62 – 64 ].…”