Personalized car following for autonomous driving with inverse reinforcement learning
Driving automation is gradually replacing human driving maneuvers in different applications
such as adaptive cruise control and lane keeping. However, contemporary driving …
such as adaptive cruise control and lane keeping. However, contemporary driving …
Inverse reinforcement learning based stochastic driver behavior learning
Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper
presents a novel driver behavior learning approach that captures the uniqueness and …
presents a novel driver behavior learning approach that captures the uniqueness and …
Predicting driver behavior on the highway with multi-agent adversarial inverse reinforcement learning
H Radtke, H Bey, M Sackmann… - 2023 IEEE Intelligent …, 2023 - ieeexplore.ieee.org
For the implementation of autonomous or highly automated driving functions, predicting the
driver behavior of the surrounding road users is highly relevant. This work investigates the …
driver behavior of the surrounding road users is highly relevant. This work investigates the …
Tackling real-world autonomous driving using deep reinforcement learning
P Maramotti, AP Capasso, G Bacchiani… - 2022 IEEE Intelligent …, 2022 - ieeexplore.ieee.org
In the typical autonomous driving stack, planning and control systems represent two of the
most crucial components in which data retrieved by sensors and processed by perception …
most crucial components in which data retrieved by sensors and processed by perception …
Car-following Behavior Modeling with Maximum Entropy Deep Inverse Reinforcement Learning
J Nan, W Deng, R Zhang, R Zhao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Modeling driving behavior plays a pivotal role in advancing the development of human-like
autonomous driving. In light of this, this paper proposes a car-following behavior modeling …
autonomous driving. In light of this, this paper proposes a car-following behavior modeling …
Robust ai driving strategy for autonomous vehicles
There has been significant progress in sensing, perception, and localization for automated
driving, However, due to the wide spectrum of traffic/road structure scenarios and the long …
driving, However, due to the wide spectrum of traffic/road structure scenarios and the long …
Generation of Adversarial Trajectories using Reinforcement Learning to Test Motion Planning Algorithms
Autonomous vehicles must be comprehensively tested before being deployed in the real
world. Simulators offer the possibility of safe, low-cost development of self-driving systems …
world. Simulators offer the possibility of safe, low-cost development of self-driving systems …
A hierarchical learning approach to autonomous driving using rule specifications
K Cho - IEEE Access, 2022 - ieeexplore.ieee.org
Understanding the movement of surrounding objects and controlling robot platforms (such
as autonomous vehicles and social robots) in a safe way are challenging problems. In the …
as autonomous vehicles and social robots) in a safe way are challenging problems. In the …
A survey on imitation learning techniques for end-to-end autonomous vehicles
The state-of-the-art decision and planning approaches for autonomous vehicles have
moved away from manually designed systems, instead focusing on the utilisation of large …
moved away from manually designed systems, instead focusing on the utilisation of large …
[HTML][HTML] Decision making for self-driving vehicles in unexpected environments using efficient reinforcement learning methods
Deep reinforcement learning (DRL) enables autonomous vehicles to perform complex
decision making using neural networks. However, previous DRL networks only output …
decision making using neural networks. However, previous DRL networks only output …