Cooperative Time and Energy-Optimal Lane Change Maneuvers for Connected Automated Vehicles

Chen, Rui; Cassandras, Christos G.; Tahmasbi-Sarvestani, Amin; Saigusa, Shigenobu; Mahjoub, Hossein Nourkhiz; Al-Nadawi, Yasir Khudhair

doi:10.1109/tits.2020.3036420

Cited by 24 publications

(23 citation statements)

References 31 publications

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“…In previous work, [16] provided a time and energy optimal solution for the maneuver shown in Fig. 1, in which the controlled vehicle C attempts to overtake an uncontrollable vehicle U by using the left lane to pass.…”

Section: Introductionmentioning

confidence: 99%

“…However, by seeking to minimize C's maneuver time, [16] adopts a vehicle-centric (selfish) viewpoint which ignores the effect of the maneuver on all remaining vehicles. As a result, since vehicle 2 typically decelerates to allow C to get ahead of it, this deceleration may cause a traffic flow slowdown in the left lane which can negatively impact throughput, especially in congested scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…As in [16], we decompose the maneuver into a longitudinal component followed by a lateral component. In the longitudinal part, our approach is based on first determining an optimal maneuver time for C subject to all safety and speed and acceleration constraints for C, 1, and 2 (see Fig.…”

Section: Introductionmentioning

confidence: 99%

“…We then solve a fixed terminal time decentralized optimal control problem for each of the two cooperating vehicles in which energy consumption is minimized while penalizing the deviation of 1 and 2 from the flow speed. In the lateral phase, we solve a decentralized optimal control problem seeking to jointly minimize the time and energy consumed which is no different than the one presented in [16]. Our analysis also allows the determination of a vehicle pair that results in minimal acceleration/deceleration for them.…”

Section: Introductionmentioning

confidence: 99%

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Sequential Cooperative Energy and Time-Optimal Lane Change Maneuvers for Highway Traffic

Armijos

Chen

Cassandras

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" only seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric). Additionally, when performing lane-changing maneuvers, we optimally select the cooperating vehicles from a set of feasible neighboring vehicles and experimentally show that the highway throughput is improved compared to the baseline case of human-driven vehicles changing lanes with no cooperation. When feasible solutions do not exist for a given maximal allowable disruption, we include a time relaxation method trading off a longer maneuver time with reduced disruption. Our analysis is also extended to multiple sequential maneuvers. Simulation results show the effectiveness of our controllers in terms of safety guarantees and up to 16% and 90% average throughput and maneuver time improvement respectively when compared to maneuvers with no vehicle cooperation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sequential Cooperative Energy and Time-Optimal Lane Change Maneuvers for Highway Traffic

Armijos

Chen

Cassandras

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Self Cite

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show abstract

“…As they used the exhaustive method to calculate the cost of each possible driving trajectory, the real-time performance is also of concern. Chen et al (2020) jointly optimized lane-changing time and energy consumption of all cooperating vehicles by optimal control methods. Despite they have adopted some methods to speed up the solution, the amount of calculation is still a problem.…”

Section: Introductionmentioning

confidence: 99%

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Ding

et al. 2022

JICV

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Purpose This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

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Automated Mobility and Cooperation Compliance in Mixed Vehicle Traffic Environments

Cassandras,

Chavez Armijos,

et al. 2024

Springer Tracts on Transportation and Traffic

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Cooperative Time and Energy-Optimal Lane Change Maneuvers for Connected Automated Vehicles

Cited by 24 publications

References 31 publications

Sequential Cooperative Energy and Time-Optimal Lane Change Maneuvers for Highway Traffic

Sequential Cooperative Energy and Time-Optimal Lane Change Maneuvers for Highway Traffic

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Automated Mobility and Cooperation Compliance in Mixed Vehicle Traffic Environments

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