Harmonious Lane Changing via Deep Reinforcement Learning

Wang, Guan; Hu, Jianming; Li, Zhiheng

doi:10.1109/tits.2020.3047129

Cited by 74 publications

(31 citation statements)

References 44 publications

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“…𝐹 𝑦,𝑡 = 𝑐 𝐿𝐶 (𝑣 𝐿 − 𝑣 𝑒𝑔𝑜,𝑦,𝑡 ) + 𝑘 𝐿𝐶 (𝑦 𝐿,𝑡 − 𝑦 𝑒𝑔𝑜,𝑡 ) (21) Where,𝑐 𝐿𝐶 and 𝑘 𝐿𝐶 indicate the virtual damping coefficient and the virtual spring stiffness between the virtual lane line and the EV, respectively. 𝑦 𝐿,𝑡 and 𝑦 𝑒𝑔𝑜,𝑡 are the velocity of lane lines and the EV at time t, respectively.…”

Section: Moving Virtual Lane Lines Based Lc Motion Planningmentioning

confidence: 99%

“…It takes about 800-man hours to mark one-hour training data [18]. To eliminate the dependence of training data, reinforcement learning has been adopted to train the LC algorithm in virtual environments by properly designing the reward function in recent years [19] [20] [21]. Since these learned LC algorithms essentially are black-box models, it is hard for these algorithms to comprehensively consider other requirements by calibration, such as fuel economic, personalized driving style, etc.…”

Section: Introductionmentioning

confidence: 99%

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Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Gao

2022

IEEE Access

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The autonomous vehicles (AVs) need to share the driving environment with the human driving vehicles (HDVs) on expressway in the future. The non-humanlike lane changing (LC) behavior of AVs can mislead human drivers, which brings potential risks. Stronger humanlike ability requires a more complex algorithm. However, the requirement of the on-board vehicle computation resources limits the humanlike ability of LC algorithms. In this context, considering environmental risks, driver speed requirements and driver focus shifting process, this paper proposes a new type of LC algorithm based on artificial potential field (APF) aiming to improve the humanlike ability. The coupling relationship between the longitudinal and the lateral potential field forces is analyzed theoretically based on environmental risk APF. Based on the relationship, we proposed a conversion mechanism of the driver between the lateral and longitudinal potential field forces to mimic the driver's speed requirement. Then a target lane selection strategy is designed to trade-off the driver's speed requirement and safety between multiple lanes. Finally, to mimic the driver's focus transfer process, the lateral forces of the ego vehicle are analyzed theoretically to further propose a moving virtual lane lines algorithm to build the lateral space varying potential field. The algorithm proposed in this paper is validated by comparing with other LC algorithms in the real traffic scenarios. The results indicate that the proposed algorithm has a better ability to mimic human driver's LC decision-making and provides a smoother and safer humanlike trajectory.INDEX TERMS Autonomous vehicles, artificial potential field, lane changing decision-making, motion planning.

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Section: Moving Virtual Lane Lines Based Lc Motion Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Gao

2022

IEEE Access

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“…Ref. [ 28 ] exploited reinforcement learning to implement the cooperative lane change approach for connected vehicles. While setting up the reward function, the proposed study considered two factors: the delay caused by an individual vehicle and traffic efficiency of the road segment.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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“…Recently, reinforcement learning (RL) has emerged as a promising framework for autonomous driving due to its online adaptation capabilities and the ability to solve complex problems [5,6]. Several recent studies have explored the use of RL in AV lane-changing [4,7,8], which consider a single AV setting where the ego vehicle learns a lanechanging behavior by taking all other vehicles as part of the driving environment for decision making. While this single-agent approach is completely scalable, it will lead to unsatisfactory performance in the complex environment like multi-AV lane-changing in mixed traffic that requires close collaboration and coordination among AVs [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Zhou

Chen

Yan

et al. 2022

Auton. Intell. Syst.

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Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving and mitigating traffic congestion, among others. Despite promising progress, lane-changing remains a great challenge for autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios. Recently, reinforcement learning (RL) has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. In this paper, we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs. Specifically, a multi-agent advantage actor-critic (MA2C) method is proposed with a novel local reward design and a parameter sharing scheme. In particular, a multi-objective reward function is designed to incorporate fuel efficiency, driving comfort, and the safety of autonomous driving. A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency, safety, and driver comfort.

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Harmonious Lane Changing via Deep Reinforcement Learning

Cited by 74 publications

References 44 publications

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

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