Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Goulet, Nathan; Ayalew, Beshah · 2022 · Crossref

DOI: 10.1109/tits.2021.3096878

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Summary

This paper addresses the challenge of improving traffic efficiency and reducing energy consumption in mixed traffic environments containing both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). The authors identify that while CAVs have the potential to harmonize traffic motion, significant challenges remain in developing distributed maneuver planning algorithms that balance individual vehicle goals with collective traffic objectives. Specifically, the research focuses on multi-lane highway domains, proposing a distributed predictive control framework that incorporates explicit coordination constraints between connected vehicles. The motivation stems from the need to utilize existing roadway infrastructure more efficiently to mitigate congestion and pollution, leveraging the computational and communication resources of CAVs rather than relying on expensive roadside infrastructure. The proposed framework consists of three main components: an Object Vehicle State Prediction (OVSP) block, a Reference Speed Assigner (RSA), and a Distributed Model Predictive Control (DMPC) block. The OVSP block estimates the current and predicted future states of vehicles in the extended neighborhood using on-board measurements and communicated information from neighboring CAVs. The RSA block estimates the instantaneous average speed of surrounding traffic and assigns a desired speed for the ego vehicle and reference speeds for each lane, facilitating direct speed harmonization. The DMPC block solves a nonlinear optimization problem over a finite horizon to determine optimal two-dimensional maneuvers, coupling lateral lane selection with longitudinal speed control. The cost function includes terms for lane-dependent tracking, lane-independent velocity tracking, predictability, and input effort, subject to safety and obstacle avoidance constraints. The study evaluates the framework through extensive traffic micro-simulations at various CAV penetration levels. The results are compared against a baseline scenario with no CAVs and a benchmark one-dimensional planner that does not couple lateral and longitudinal decisions. The findings demonstrate that the proposed distributed control framework significantly improves traffic flow, reduces travel time, and lowers fuel consumption compared to the baselines. Specifically, the integration of a two-dimensional maneuver planner allows for more intelligent speed harmonization by considering lane changes alongside speed adjustments, isolating the benefits of coupled planning over traditional cooperative adaptive cruise control methods. The simulations confirm that direct speed harmonization, achieved by tracking estimated average traffic speeds, yields greater improvements in travel time and fuel efficiency than indirect methods that rely solely on tracking immediate neighbors. The significance of this work lies in its demonstration that distributed, on-board computation can effectively coordinate CAV maneuvers to boost overall traffic efficiency without requiring centralized roadside infrastructure. By validating the benefits of coupling lateral and longitudinal control in a distributed setting, the paper provides a practical pathway for deploying CAVs in mixed traffic. The results suggest that even at partial penetration levels, such coordinated control schemes can mitigate congestion and reduce energy consumption, offering a scalable solution for improving transportation sustainability and efficiency in the foreseeable future.

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• v2x connected vehicle