Visual Detection and Deep Reinforcement Learning-Based Car Following and Energy Management for Hybrid Electric Vehicles

Chen, Jiaxin; Yang, Kai; Wei, Chongfeng; Grzesiak, Lech M.; Hu, Xiaosong

doi:10.1109/tte.2022.3141780

Cited by 52 publications

(22 citation statements)

References 47 publications

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“…In the cost comparison, the proposed strategy is contrasted with DDPG-based EMS without battery degradation consideration and DQL-based EMS with battery degradation consideration. DQL algorithm is a DRL algorithm that handles segmented action states and has been shown to achieve good control results in the EMS of hybrid vehicles [15,45,46]. Under this premise, the cost of different strategies per 100 km under the three real-world usage scenarios is listed in Table 11.…”

Section: Validation Of Overall Driving Cost Optimization Andmentioning

confidence: 99%

Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios

Han

Zhao

et al. 2023

International Journal of Energy Research

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Energy management strategy (EMS) is a way to reduce the energy consumption of hybrid power systems. This article proposes a unique deep reinforcement learning- (DRL-) based EMS for plug-in hybrid electric heavy-duty trucks (PHETs), combining driving cycle pattern recognition (DPR) and deep transfer learning (DTL). The proposed EMS can cope well with the complex usage scenarios of PHETs and the difficulty of generating EMS. While ensuring the minimum overall driving cost, the strategy can improve the convergence speed of the DRL method and the generalizability under segmented usage scenarios. Firstly, representative driving cycles that reflect different usage scenarios are constructed based on a naturalistic data-driven method. Secondly, a plug-in hybrid electric heavy-duty truck (PHET) driving pattern recognizer based on a learning vector quantization neural network (LVQ) is built. Thirdly, the deep deterministic policy gradient (DDPG) algorithm is innovatively combined with the DTL algorithm. The pretrained neural network in the corresponding usage scenarios is transferred to the natural driving cycles based on DTL. Moreover, the proposed EMS gives an emphasized consciousness on the battery degradation cost. Finally, the strategy is tested under natural driving cycles in different usage scenarios and proven through comparison with the current state-of-the-art techniques, deep reinforcement learning-based strategy, and dynamic programming (DP). The results show that the proposed strategy outperforms existing cutting-edge deep reinforcement learning techniques in terms of convergence speed, battery life extension, fuel consumption, and overall driving cost reduction. The proposed control strategy can improve the convergence speed by nearly 50%, while effectively extending the battery life and reducing the overall driving cost compared to the existing state-of-the-art strategies. The battery degradation rate is reduced by 48.46%, 57.95%, and 36.99%, and the driving cost is reduced by 17.76%, 8.51%, and 7.12%, respectively, under each usage scenario.

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Section: Validation Of Overall Driving Cost Optimization Andmentioning

confidence: 99%

Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios

Han

Zhao

et al. 2023

International Journal of Energy Research

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“…However, due to the high training requirements of deep reinforcement learning, the depth of environment perception neural network of deep reinforcement learning cannot be designed too deep, resulting in the limited driving environment state that the reinforcement learning method can perceive under low-light conditions, and it is unable to make driving behavior decisions usually. Currently, the target detection method has been widely used as an auxiliary reinforcement learning task [45]- [47]. The SETR-YOLOv5n target detection method proposed in this paper can quickly detect the lane's position and the lane's bending angle under low-light conditions, mark it in the video frame and transmit the video frame to the deep reinforcement learning algorithm as an autonomous driving environment.…”

Section: Application In Automatic Drivingmentioning

confidence: 99%

SETR-YOLOv5n: A Lightweight Low-Light Lane Curvature Detection Method Based on Fractional-Order Fusion Model

Liu

Wang

Li³

et al. 2022

IEEE Access

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End-to-end automatic driving requires the identification of lane curvature. We proposed a lightweight detection method for the low-light lane curvature based on the Fractional-Order Fusion Model (FFM) to assure real-time performance and increase the reliability of automatic driving in lowlight conditions. To begin, the FFM method is introduced to enhance images with low average brightness, fuzzy detail, and a high signal-to-noise ratio. Under low-light conditions, these images cannot clearly express information (such as rainy, snowy, foggy, and other harsh external environments). Then, aiming at the problems of complex network structure, high hardware configuration required for training, and low transmission Frames Per Second (FPS) of real-time detection in the previously proposed YOLOv5, the SETR-C3Block module is proposed. The YOLOv5n is improved by optimizing the configuration of the target detector head and the network's structure, which solves the problems of low efficiency and redundancy parameters in feature extraction in the network. According to the experimental results on the lane curvature dataset, the mAP@.5:.95 of SETR-YOLOv5n is 87.22%, the transmission FPS of real-time detection is 70.4, and the number of model parameters is only 1.8M. It shows that the SETR-YOLOv5n can meet the lightweight and accuracy requirements of target detection by the mobile terminal or embedded device.

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“…In a test, they achieved 96.5% fuel economy of the global optimum with dynamic programming, increasing fuel efficiency by up to 8.8% over an existing method. In [33], the authors propose a different approach to reduce fuel consumption. They proposed a DQN-based car-following strategy and a learning-based energy management strategy to achieve a low fuel consumption while maintaining a safe real-time distance.…”

Section: Deep Learning For Computer Visionmentioning

confidence: 99%

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Huang¹,

Ren²

2022

J. Engg. Res. & Sci.

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Autonomous vehicles are the future of road traffic. In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver. To realize this vision, seven essential technologies need to be evolved and refined including path planning, computer vision, sensor fusion, data security, fault diagnosis, control, and lastly, communication and networking. The contributions and the novelty of this paper are: 1) provide a comprehensive review of the recent advances in using deep learning for autonomous vehicle research, 2) offer insights into several important aspects of this emerging area, and 3) identify five directions for future research. To the best of our knowledge, there is no previous work that provides similar reviews for autonomous vehicle design.

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Visual Detection and Deep Reinforcement Learning-Based Car Following and Energy Management for Hybrid Electric Vehicles

Cited by 52 publications

References 47 publications

Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios

Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios

SETR-YOLOv5n: A Lightweight Low-Light Lane Curvature Detection Method Based on Fractional-Order Fusion Model

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

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