Autonomous Emergency Steering Using Deep Reinforcement Learning For Advanced Driver Assistance System

Yoshimura, M; Fujimoto, Gakuyo; Kaushik, Abinav; Padi, Bharat; Dennison, Matthew; Sood, Ishaan; Sarkar, Kinsuk; Muneer, Abdul; More, Amit; Tsuchiya, Masamitsu; Araki, Satoru; Hebber, Anil; Tieleman, Tijmen; Yasui, Yoshiaki

doi:10.23919/sice48898.2020.9240358

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…This research is highly relevant to our work as it demonstrates the use of CNNs for object detection, an essential component for autonomous emergency response systems. Yoshimura et al [15] implemented deep reinforcement learning for autonomous emergency steering in advanced driver assistance systems. Their approach highlights the effectiveness of reinforcement learning in making real-time navigation decisions, aligning closely with our proposed decision-making framework.…”

Section: Related Workmentioning

confidence: 99%

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Needhi

2024

IJSREM

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Abstract—This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learn- ing, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services. Index Terms—Object Detection, Convolutional Neural Net- works (CNN), Reinforcement Learning, Q-Learning, AI-Driven Ambulance, Autonomous Systems, Emergency Medical Services, Real-Time Navigation.

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Section: Related Workmentioning

confidence: 99%

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Needhi

2024

IJSREM

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“…The MDP modeling paradigm can be extended to a partially observable Markov decision process (POMDP), as shown in [13], which uses classic dynamic programming for path planning. Though, the pre-trained agents do not necessarily need to generate a path to follow, as shown in [14], where an end-to-end solution is presented for emergency steering by using the proximal policy optimization (PPO) algorithm. This end-to-end approach can be used for different applications, such as for underwater vehicles [15].…”

Section: Related Workmentioning

confidence: 99%

Online Trajectory Planning with Reinforcement Learning for Pedestrian Avoidance

2022

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Planning the optimal trajectory of emergency avoidance maneuvers for highly automated vehicles is a complex task with many challenges. The algorithm needs to decrease accident risk by reducing the severity and keeping the car in a controllable state. Optimal trajectory generation considering all aspects of vehicle and environment dynamics is numerically complex, especially if the object to be avoided is moving. This paper presents a hierarchical method for the avoidance of moving objects in an autonomous vehicle, where a reinforcement learning agent is responsible for local planning, while longitudinal and lateral control is performed by the low-level model-predictive controller and Stanley controllers. In the developed architecture, the agent is responsible for the optimization. It is trained in various scenarios to provide the necessary parameters for a polynomial-based path and a velocity profile in a neural network output. The vehicle performs only the first step of the trajectory, which is redesigned repeatedly by the planner based on the new state. In the training phase, the vehicle executes the entire trajectory via low-level controllers to determine the reward value, which realizes a prediction for the future. The agent receives feedback and can further improve its performance. Finally, the proposed framework was tested in a simulation environment and was also compared to human drivers’ abilities.

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Learning Terminal State of the Trajectory Planner: Application for Collision Scenarios of Autonomous Vehicles

Lim,

Lee,

Shin

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Autonomous Emergency Steering Using Deep Reinforcement Learning For Advanced Driver Assistance System

Cited by 6 publications

References 4 publications

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Online Trajectory Planning with Reinforcement Learning for Pedestrian Avoidance

Learning Terminal State of the Trajectory Planner: Application for Collision Scenarios of Autonomous Vehicles

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