Intelligent vehicle decision-making and trajectory planning method based on deep reinforcement learning in the Frenet space
J Wang, L Chu, Y Zhang, Y Mao, C Guo - Sensors, 2023 - mdpi.com
The complexity inherent in navigating intricate traffic environments poses substantial hurdles
for intelligent driving technology. The continual progress in mapping and sensor …
for intelligent driving technology. The continual progress in mapping and sensor …
Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making
There are still some problems need to be solved though there are a lot of achievements in
the fields of automatic driving. One of those problems is the difficulty of designing a car …
the fields of automatic driving. One of those problems is the difficulty of designing a car …
[PDF][PDF] Inverse reinforcement learning for robotic applications: hidden variables, multiple experts and unknown dynamics
KD Bogert - 2016 - getd.libs.uga.edu
Robots deployed into many real-world scenarios are expected to face situations that their
designers could not anticipate. Machine learning is an effective tool for extending the …
designers could not anticipate. Machine learning is an effective tool for extending the …
[PDF][PDF] Autonomous vehicle control via deep reinforcement learning
S Kardell, M Kuosku - 2017 - odr.chalmers.se
The automotive industry as well as academia are currently conducting a lot of research
related to autonomous driving. Autonomous driving is an interesting topic that holds the …
related to autonomous driving. Autonomous driving is an interesting topic that holds the …
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
Predicting the motion of a mobile agent from a third-person perspective is an important
component for many robotics applications, such as autonomous navigation and tracking …
component for many robotics applications, such as autonomous navigation and tracking …
Achieving accurate trajectory predicting and tracking for autonomous vehicles via reinforcement learning-assisted control approaches
T Guangwen, L Mengshan, H Biyu, Z Jihong… - … Applications of Artificial …, 2024 - Elsevier
In complex urban traffic scenarios, autonomous vehicles face significant challenges in
adapting to diverse and dynamic traffic conditions. Reward-based reinforcement learning …
adapting to diverse and dynamic traffic conditions. Reward-based reinforcement learning …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Studies on drivers' driving styles based on inverse reinforcement learning
Although advanced driver assistance systems (ADAS) have been widely introduced in
automotive industry to enhance driving safety and comfort, and to reduce drivers' driving …
automotive industry to enhance driving safety and comfort, and to reduce drivers' driving …
A critical state identification approach to inverse reinforcement learning for autonomous systems
Inverse reinforcement learning features a reward function based on reward features and
positive demonstrations. When complex learning tasks are performed, the entire state space …
positive demonstrations. When complex learning tasks are performed, the entire state space …
Autonomous driving at the handling limit using residual reinforcement learning
While driving a vehicle safely at its handling limit is essential in autonomous vehicles in
Level 5 autonomy, it is a very challenging task for current conventional methods. Therefore …
Level 5 autonomy, it is a very challenging task for current conventional methods. Therefore …