Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach
Reinforcement learning has demonstrated its potential in a series of challenging domains.
However, many real-world decision making tasks involve unpredictable environmental …
However, many real-world decision making tasks involve unpredictable environmental …
Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach
Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex
decision making behaviors through interacting with other traffic participants. However, many …
decision making behaviors through interacting with other traffic participants. However, many …
Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …
ensuring trustworthiness remains a formidable challenge when applying this technology to …
Improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning
X Ma, K Driggs-Campbell… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
To improve efficiency and reduce failures in autonomous vehicles, research has focused on
developing robust and safe learning methods that take into account disturbances in the …
developing robust and safe learning methods that take into account disturbances in the …
Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
Randomized adversarial imitation learning for autonomous driving
With the evolution of various advanced driver assistance system (ADAS) platforms, the
design of autonomous driving system is becoming more complex and safety-critical. The …
design of autonomous driving system is becoming more complex and safety-critical. The …
Adversarial evaluation of autonomous vehicles in lane-change scenarios
Autonomous vehicles must be comprehensively evaluated before deployed in cities and
highways. However, most existing evaluation approaches for autonomous vehicles are static …
highways. However, most existing evaluation approaches for autonomous vehicles are static …
Adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles
V Behzadan, A Munir - IEEE Intelligent Transportation Systems …, 2019 - ieeexplore.ieee.org
With the rapidly growing interest in autonomous navigation, the body of research on motion
planning and collision avoidance techniques has enjoyed an accelerating rate of novel …
planning and collision avoidance techniques has enjoyed an accelerating rate of novel …
Cat: Closed-loop adversarial training for safe end-to-end driving
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling
accident-prone traffic events by algorithm designs at the policy level, we investigate a …
accident-prone traffic events by algorithm designs at the policy level, we investigate a …
Proximal policy optimization through a deep reinforcement learning framework for multiple autonomous vehicles at a non-signalized intersection
D Quang Tran, SH Bae - Applied Sciences, 2020 - mdpi.com
Advanced deep reinforcement learning shows promise as an approach to addressing
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …