Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

Zhu, Hong; Han, Tianyang; Alhajyaseen, Wael; Iryo-Asano, Miho; Nakamura, Hideki

doi:10.1016/j.aap.2022.106711

Cited by 13 publications

(3 citation statements)

References 46 publications

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“…Zhu et al [ 78 ] attempted to create automated driving control models that can prevent safety hazards caused by distracted pedestrians with different crossing speeds at unsignalized mid-block crosswalks. They developed an original rule-based model that assumes all detected pedestrians are distracted and an original learning-based model using reinforcement learning methods with a comprehensive reward function that considers failure cases and acceptable minimum safety margin time for pedestrians.…”

Section: Marl For Cavsmentioning

confidence: 99%

A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles

Yadav

Mishra

Kim

2023

Sensors

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Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in MARL for CAVs. This paper provides a comprehensive survey on MARL for CAVs. A classification-based paper analysis is performed to identify the current developments and highlight the various existing research directions. Finally, the challenges in current works are discussed, and some potential areas are given for exploration to overcome those challenges. Future readers will benefit from this survey and can apply the ideas and findings in their research to solve complex problems.

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Section: Marl For Cavsmentioning

confidence: 99%

A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles

Yadav

Mishra

Kim

2023

Sensors

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“…Tang et al [22] introduced a motion planning method for automated driving using a soft actor-critic method [23], in which different strategies were balanced via the weights of safety, comfort, and efficiency. Zhu et al [24] developed a motion planning method to consider the factor of pedestrian distraction based on a rule-based method and a learning-based method. Their experimental results demonstrated that the learning-based method may be better at handling the unsafe action problem at unsignalized mid-block crosswalks than the rule-based method; nonetheless, the learningbased method may generate unreasonable actions.…”

Section: Related Workmentioning

confidence: 99%

Constrained Reinforcement Learning for Vehicle Motion Planning with Topological Reachability Analysis

Chen

Zhang

et al. 2022

Robotics

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Rule-based traditional motion planning methods usually perform well with prior knowledge of the macro-scale environments but encounter challenges in unknown and uncertain environments. Deep reinforcement learning (DRL) is a solution that can effectively deal with micro-scale unknown and uncertain environments. Nevertheless, DRL is unstable and lacks interpretability. Therefore, it raises a new challenge: how to combine the effectiveness and overcome the drawbacks of the two methods while guaranteeing stability in uncertain environments. In this study, a multi-constraint and multi-scale motion planning method is proposed for automated driving with the use of constrained reinforcement learning (RL), named RLTT, and comprising RL, a topological reachability analysis used for vehicle path space (TPS), and a trajectory lane model (TLM). First, a dynamic model of vehicles is formulated; then, TLM is developed on the basis of the dynamic model, thus constraining RL action and state space. Second, macro-scale path planning is achieved through TPS, and in the micro-scale range, discrete routing points are achieved via RLTT. Third, the proposed motion planning method is designed by combining sophisticated rules, and a theoretical analysis is provided to guarantee the efficiency of our method. Finally, related experiments are conducted to evaluate the effectiveness of the proposed method; our method can reduce 19.9% of the distance cost in the experiments as compared to the traditional method. Experimental results indicate that the proposed method can help mitigate the gap between data-driven and traditional methods, provide better performance for automated driving, and facilitate the use of RL methods in more fields.

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“…Research on detection and localization algorithms with robust target detection capabilities in complex natural environments is necessary. Deep-learning techniques have been widely used in visual detection [11][12][13][14][15]. In the field of target detection, mainstream target detection algorithms are usually categorized into single-stage target detection, e.g., the YOLO [16] (you only look once) series and SSD [17] (single shot multibox detector), and two-stage target detection, e.g., RCNN (Region CNN) [18], Fast RCNN [19], and Faster RCNN [20].…”

Section: Introductionmentioning

confidence: 99%

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

Zhang,

Tian,

Yin

et al. 2024

Agriculture

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The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

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Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

Cited by 13 publications

References 46 publications

A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles

A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles

Constrained Reinforcement Learning for Vehicle Motion Planning with Topological Reachability Analysis

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

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