arXiv research

The study improves deep learning models for safer autonomous vehicles.

The paper reviews machine learning safety techniques for autonomous vehicles.

Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicl…

We enhance autonomous materials research with problem-aware models.

This paper presents a novel approach for automatic rule learning applicable to an autonomous driving system using real driving data.

Safe RL for autonomous vehicles using PCPO with trust regions and parallel learners.

ApolloRL offers a platform for RL research in autonomous driving.

A machine learning environment for detecting autonomous vehicle corner cases.

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 environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system…

Robots learn new tasks autonomously with minimal human intervention.

Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.

This paper explores formal verification for autonomous systems, identifying limitations and proposing improvements.

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of…

Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step requ…

Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.

Robot science discovers new materials faster.

While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessibl…

The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep…

Improved UAV navigation and landing using deep learning.

End-to-end autonomous driving perception learns latent features for better performance.

New model enables AI to learn autonomously.

We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance …

Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have attempted to address this issue, but there are few publicly-available tools to adequately explore the trade-offs between functionality, scal…

SECRM-2D improves RL-based autonomous driving with safety guarantees.

Deep learning agent improves pedestrian navigation in urban environments.

Automated testing framework finds weaknesses in deep control policies.

The Surprise index assesses autonomous systems' competency in uncertain environments.

This paper compares uncertainty estimation methods for deep learning in autonomous vehicles.

Deep learning predicts vehicle behavior for safer autonomous driving.

Develops a method to simulate rare dangerous events in autonomous systems.

This paper surveys DRL for autonomous vehicle motion planning.

Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be interrelated (or "hierarchical") so that a robot has to first learn to achieve some of them to set the preconditions for learning other ones. …

This work improves safety validation of autonomous vehicles by finding interpretable failures.

Develops methods to improve reliability of deep learning for autonomous driving.

Future autonomous systems need reliable world models and complex action sequences.

New approach decouples skill learning and language grounding for autonomous agents.

Deep Recurrent Q-Network improves autonomous driving in urban areas with pedestrians.

This research introduces an autonomous robot navigation method using reinforcement learning.

Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial…

Proves rigidity of 3D partially hyperbolic systems via autonomous dynamics.

Bayesian reflex models AI learning like the autonomic nervous system.

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthe…

There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications. Recent work in safe reinforcement learning uses idealized models to achieve their guarantees, but these models do …

Advances in deep learning and reinforcement learning enable autonomous UAS.

Study learns dynamics of linear systems from multiple short trajectories.

A hybrid model combines Q-learning and PID controller for continuous vehicle control.

Active IRL selects optimal human demonstrations for learning AI preferences.

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have b…