Autonomous Intersection Management for Connected and Automated Vehicles: A Lane-Based Method

Most existing studies on autonomous intersection management (AIM) often focus algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and exit of are exogenous inputs. This paper shows allowing lanes be optimized can significantly improve traffic efficiency. In particular, this proposes “all-direction” lanes, where left-turn, through, right-turn is all allowed at same lane. We develop two methods for optimizing entering time (i.e., when enter intersection) route choice decisions lane), including sliding-time-window-based global optimum (GO-STW) first-come-first-served method with optimal choices (FCFS-R). The developed lane-based formulated as mixed integer linear programming (MILP) problems, which solved using CPLEX solver. A heuristic further adopted solve MILP model in a timely manner, illustrates potential real-time applicability proposed method. Numerical analysis conducted examine performance effectiveness heuristic. found optimization lane/route more critical than time.