Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

Du, Yuchuan; Chen, Jianli; Zhao, Cong; Liu, Chenglong; Liao, Feixiong; Chan, Ching-Yao

doi:10.1016/j.trc.2021.103489

Cited by 68 publications

(43 citation statements)

References 41 publications

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“…In addition, travel efficiency and comfort are evaluated by average speed and the absolute value of jerk, respectively, and headway is used to illustrate safety, which are consistent with the evaluation methods of other related studies (Li et al , 2021; Du et al , 2022; Srisomboon and Lee, 2021).…”

Section: Results and Analysismentioning

confidence: 83%

“…In recent years, reinforcement learning (RL) has shown its great advantages in computing speed and dealing with complex scenario tasks in the field of autonomous driving. Zhu et al (2020), Li et al (2021), Du et al (2022), Wang et al (2022) have confirmed that RL runs much faster than model predictive control (even more than 200 times faster), which holds a great promise for real-time implementations. Kendall et al (2019) demonstrated the first application of RL to a full-sized autonomous driving in real-world driving experiments.…”

Section: Reinforcement Learningmentioning

confidence: 80%

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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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Section: Results and Analysismentioning

confidence: 83%

Section: Reinforcement Learningmentioning

confidence: 80%

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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“…Buechel and Knoll developed a DDPG-based predictive longitudinal controller that directly selects accelerations according to reference speeds and road grades [11]. Subsequently, the authors of this study have used the DDPG algorithm to control the speed with prior knowledge of the dynamic speed limit and comfortable speeds on rough pavements [12]. However, it only provides a solution to a multi-objective speed control problem for an AV without consideration of surrounding vehicles.…”

Section: Introductionmentioning

confidence: 99%

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System

Chen

Zhao

Jiang

et al. 2023

IJERPH

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Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.

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“…With the rapid development of the Internet of ings (IoT) [1], advanced sensors [2], and connected and automated vehicles [3], the demand for high-accuracy location-based services has exploded. Among these, smart indoor parking lots (mostly indoor) are typical integrated applications of the IoT, sensors, and location services, improving the use of parking resources and user satisfaction, especially with the advent of automated valet parking (AVP) [4].…”

Section: Introductionmentioning

confidence: 99%

A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems

Jiang

Zhao

Zhu

et al. 2022

Journal of Advanced Transportation

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Automated valet parking (AVP) has attracted much attention as the entry point to autonomous driving. In an indoor environment, high-precision positioning systems are essential for AVP. Ultra-wideband (UWB) is one of the most widely adopted techniques. However, the base station placement significantly influences the system’s positioning accuracy, especially for the irregular architecture of underground parking lots. This article proposes a three-stage practical and economical layout planning approach for UWB base stations, including determining the deployment strategy and layout parameters and comprehensive adjustment and scheme verification. The approach considers regional differentiation accuracy requirements for AVP, such as ramp area, surface fluctuation area, and narrow area. The adopted positioning method of a UWB system is the time difference of arrival (TDOA), and the evaluation index of positioning accuracy is the horizontal dilution of precision (HDOP). Through experimental tests in an actual parking lot, the proposed approach is confirmed to ensure stability and economy with fewer UWB base stations and can meet the positioning accuracy requirements of AVP.

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Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

Cited by 68 publications

References 41 publications

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

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

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System

A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems

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