Mars: An instance-aware, modular and realistic simulator for autonomous driving
Nowadays, autonomous cars can drive smoothly in ordinary cases, and it is widely
recognized that realistic sensor simulation will play a critical role in solving remaining corner …
recognized that realistic sensor simulation will play a critical role in solving remaining corner …
Learning highway ramp merging via reinforcement learning with temporally-extended actions
Several key scenarios, such as intersection navigation, lane changing, and ramp merging,
are active areas of research in autonomous driving. In order to properly navigate these …
are active areas of research in autonomous driving. In order to properly navigate these …
Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for
urban autonomous driving where there are complex contextual factors, such as lane …
urban autonomous driving where there are complex contextual factors, such as lane …
Flow: A modular learning framework for mixed autonomy traffic
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation
systems through improved safety, efficiency, and access to mobility. However, the …
systems through improved safety, efficiency, and access to mobility. However, the …
Overtaking maneuvers in simulated highway driving using deep reinforcement learning
Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …
usually try to either directly minimize an objective function or iteratively in a Reinforcement …
Driving everywhere with large language model policy adaptation
Adapting driving behavior to new environments customs and laws is a long-standing
problem in autonomous driving precluding the widespread deployment of autonomous …
problem in autonomous driving precluding the widespread deployment of autonomous …
Adaptive behavior generation for autonomous driving using deep reinforcement learning with compact semantic states
Making the right decision in traffic is a challenging task that is highly dependent on
individual preferences as well as the surrounding environment. Therefore it is hard to model …
individual preferences as well as the surrounding environment. Therefore it is hard to model …
Trafficgen: Learning to generate diverse and realistic traffic scenarios
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous
driving systems in simulation. This work introduces a data-driven method called TrafficGen …
driving systems in simulation. This work introduces a data-driven method called TrafficGen …
Zero-shot autonomous vehicle policy transfer: From simulation to real-world via adversarial learning
In this article, we demonstrate a zero-shot transfer of an autonomous driving policy from
simulation to University of Delaware's scaled smart city with adversarial multi-agent …
simulation to University of Delaware's scaled smart city with adversarial multi-agent …
Learning hierarchical behavior and motion planning for autonomous driving
Learning-based driving solution, a new branch for autonomous driving, is expected to
simplify the modeling of driving by learning the underlying mechanisms from data. To …
simplify the modeling of driving by learning the underlying mechanisms from data. To …