Optimal Platoon Trajectory Planning Approach at Arterials

Abstract

Cooperative (automated) vehicles have the potential to enhance traffic efficiency and fuel economy on urban roads, especially at signalized intersections. An optimal control approach to optimize the trajectories of cooperative vehicles at fixed-timing signalized intersections along an arterial is presented. The proposed approach aims to optimize throughput first, and then to maximize comfort while minimizing travel delay and fuel consumption. The proposed approach is flexible in dealing with both quadratic and more complex cost functions. Assuming fixed timing signal control in a cycle and vehicle-to-infrastructure communication, the red phase is taken into account in position constraints for vehicles that cannot pass the intersection in the green phase. Safety is guaranteed by constraining the inter-vehicle distance larger than some desired value. The approach is scalable and can be used for joint trajectory planning of one platoon approaching another stationary platoon. It can also be extended to multiple intersections with fixed signal plans. To verify the performances of the controlled platoon, simulation under three different traffic scenarios is conducted, namely: an isolated intersection with/without downstream vehicle queues, and platoon control at multiple intersections. Three baseline scenarios without control are also designed to compare performances in relation to both mobility and fuel consumption in each controlled scenario. The results demonstrate that the controlled vehicles generate plausible behavior under control objectives and constraints. Moreover, the consideration of downstream vehicle queues and the application at both an isolated signalized intersection and arterial corridors on urban roads verify the flexible characteristics of the control framework.

Receding Horizon Cooperative Platoon Trajectory Planning on Corridors with Dynamic Traffic Signal

“…The illustration of the solution approach is depicted in Figure 1. The MPC framework is applied, which solves the control problem in a shorter horizon than the optimal control framework in our previous work (29). This shorter horizon of the MPC framework results in an efficient computational time.…”

“…Therefore, more attention should be paid to design the target speed in an optimal way, and relieve computational burden when tracking the piecewise target speed cost term. Another approach was to regard the red phase as a position constraint that the stopping vehicle could not pass (29). However, the work in Liu et al (29) did not track the preceding vehicles in desired gaps.…”

“…Another approach was to regard the red phase as a position constraint that the stopping vehicle could not pass (29). However, the work in Liu et al (29) did not track the preceding vehicles in desired gaps. Therefore, elaborate work on tuning cost weights was necessary to make a trade-off between maximizing speeds and minimizing fuel consumption.…”

“…The feedback at each time step in the closed-loop can replan the trajectory under actuated or semi-actuated signal plans. In addition, the previous work in Liu et al (29) required elaborate work on tuning cost weights to avoid stopping away from the stop-line. An improvement to address this problem is to transform the red phase position constraint to a penalty term in the running cost, which helps tune the cost weights under the workings of both the safe following and red phase terms.…”

Platoon forming algorithms for intelligent street intersections

“…In the existing literature, many more models and control techniques are investigated like reservation based control algorithms (Dresner and Stone 2008) and controls based on fuzzy logic (Milanés et al 2010). In a recent paper, Liu, Wang, and Hoogendoorn (2019) present a method for fixed-cycle plans, where the PFA and the optimization of the trajectories are integrated. For an overview we refer to Rios-Torres and Malikopoulos (2017).…”