A Decision-Making Strategy for Vehicle Autonomous Braking in Emergency via Deep Reinforcement Learning

Fu, Yuchuan; Li, Changle; Yu, F. Richard; Luan, Tom H.; Zhang, Yao

doi:10.1109/tvt.2020.2986005

Cited by 104 publications

(45 citation statements)

References 27 publications

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“…The model was trained for 9000 epochs and achieved an obstacle avoidance rate of 95%. The autonomous braking problem is analyzed and discussed in [38] through precise decision-making and control to reduce accidents. They proposed a Deep Reinforcement Learning-based autonomous braking system in emergencies.…”

Section: ) Reinforcement Learningmentioning

confidence: 99%

“…One of the largest and widely used benchmark datasets in the autonomous driving research community [8], [12], [25], [27], [37], [38] is the KITTI dataset [39], which provides LiDAR point clouds, stereo color, and grayscale pictures, and GPS coordinates. The data was captured on the highways and rural areas of a mid-sized city in Germany called Karlsruhe.…”

Section: A the Kitti Datasetmentioning

confidence: 99%

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Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges

Jebamikyous

Kashef

2022

IEEE Access

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Perception is the fundamental task of any autonomous driving system, which gathers all the necessary information about the surrounding environment of the moving vehicle. The decision-making system takes the perception data as input and makes the optimum decision given that scenario, which maximizes the safety of the passengers. This paper surveyed recent literature on autonomous vehicle perception (AVP) by focusing on two primary tasks: Semantic Segmentation and Object Detection. Both tasks play an important role as a vital component of the vehicle's navigation system. A comprehensive overview of deep learning for perception and its decision-making process based on images and LiDAR point clouds is discussed. We discussed the sensors, benchmark datasets, and simulation tools widely used in semantic segmentation and object detection tasks, especially for autonomous driving. This paper acts as a road map for current and future research in AVP, focusing on models, assessment, and challenges in the field.

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Section: ) Reinforcement Learningmentioning

confidence: 99%

Section: A the Kitti Datasetmentioning

confidence: 99%

Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges

Jebamikyous

Kashef

2022

IEEE Access

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“…Suddenly changing lanes and braking of the leading vehicle, caused by driver's distraction, misjudgment, and misoperation, increase the risk of accidents. An autonomous braking decision-making strategy was proposed with deep reinforcement learning in [146] to facilitate low-level control of CAVs in emergency situations. Despite promising as the core of next-generation intelligent transportation systems, CAVs will face some hidden security problems relating to the complicated AI configurations/settings of autopilot systems [147], where end-users with limited AI experience and knowledge can be a nuisance and may cause accidents and risky dangers.…”

Section: Connected Autonomous Vehiclesmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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“…However the idea of creating datasets to mimic real-world behaviour can be extended to driving policies in order to make policies more naturalistic. Y.Fu [7]. examines Deep Deterministic Policy Gradient (DDPG) algorithm based on Actor-Critic infrastructure to reduce the difficulty of learning in more complex environments.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving

Dubey

Kasad

Agrawal

2021

Proceedings of the 13th International Conference on Agents and Artificial Intelligence

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Autonomous Braking and Throttle control is key in developing safe driving systems for the future. There exists a need for autonomous vehicles to negotiate a multi-agent environment while ensuring safety and comfort. A Deep Reinforcement Learning based autonomous throttle and braking system is presented. For each time step, the proposed system makes a decision to apply the brake or throttle. The throttle and brake are modelled as continuous action space values. We demonstrate 2 scenarios where there is a need for a sophisticated braking and throttle system, i.e when there is a static obstacle in front of our agent like a car, stop sign. The second scenario consists of 2 vehicles approaching an intersection. The policies for brake and throttle control are learned through computer simulation using Deep deterministic policy gradients. The experiment shows that the system not only avoids a collision, but also it ensures that there is smooth change in the values of throttle/brake as it gets out of the emergency situation and abides by the speed regulations, i.e the system resembles human driving.

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A Decision-Making Strategy for Vehicle Autonomous Braking in Emergency via Deep Reinforcement Learning

Cited by 104 publications

References 27 publications

Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges

Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving

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