An empirical study of ddpg and ppo-based reinforcement learning algorithms for autonomous driving
S Siboo, A Bhattacharyya, RN Raj, SH Ashwin - IEEE Access, 2023 - ieeexplore.ieee.org
Autonomous vehicles mitigate road accidents and provide safe transportation with a smooth
traffic flow. They are expected to greatly improve the quality of the elderly or people with …
traffic flow. They are expected to greatly improve the quality of the elderly or people with …
Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance
SH Ashwin, R Naveen Raj - International Journal of Information …, 2023 - Springer
Numerous accidents and fatalities occur every year across the world as a result of the
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …
Decision-making for autonomous vehicles on highway: Deep reinforcement learning with continuous action horizon
Decision-making strategy for autonomous vehicles de-scribes a sequence of driving
maneuvers to achieve a certain navigational mission. This paper utilizes the deep …
maneuvers to achieve a certain navigational mission. This paper utilizes the deep …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach
In the early diffusion stage of autonomous vehicle systems, the controlling of vehicles
through exacting decision-making to reduce the number of collisions is a major problem …
through exacting decision-making to reduce the number of collisions is a major problem …
Prediction based decision making for autonomous highway driving
Autonomous driving decision-making is a challenging task due to the inherent complexity
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
Deep reinforcement learning reward function design for autonomous driving in lane-free traffic
Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the
notion of lanes, and consider the whole lateral space within the road boundaries. This …
notion of lanes, and consider the whole lateral space within the road boundaries. This …
Dyna-PPO reinforcement learning with Gaussian process for the continuous action decision-making in autonomous driving
G Wu, W Fang, J Wang, P Ge, J Cao, Y Ping, P Gou - Applied Intelligence, 2023 - Springer
Recent years have witnessed rapid development of autonomous driving. Model-based and
model-free reinforcement learning are two popular learning methods for autonomous …
model-free reinforcement learning are two popular learning methods for autonomous …
Porf-ddpg: Learning personalized autonomous driving behavior with progressively optimized reward function
J Chen, T Wu, M Shi, W Jiang - Sensors, 2020 - mdpi.com
Autonomous driving with artificial intelligence technology has been viewed as promising for
autonomous vehicles hitting the road in the near future. In recent years, considerable …
autonomous vehicles hitting the road in the near future. In recent years, considerable …
Vehicles control: Collision avoidance using federated deep reinforcement learning
In the face of growing urban populations and the escalating number of vehicles on the
roads, managing transportation efficiently and ensuring safety have become critical …
roads, managing transportation efficiently and ensuring safety have become critical …