A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, formulating a rule based decision maker for selecting maneuvers may not be ideal. Similarly, it may not be effective to design an a-priori cost…
Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level …
Safe RL for autonomous vehicles using PCPO with trust regions and parallel learners.
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 …
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…
A new RL model ensures safe learning in uncertain environments.
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of coop…
New risk metric for AI systems reduces safety risks with minimal data.
The paper tackles safe reinforcement learning with convex regularization.
Counterfactual policy evaluation improves autonomous driving policies' generalization.
Proposes Constrained Q-learning for reinforcement learning with constraints.
BagCert efficiently certifies robustness against adversarial patches on image classifiers.
Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erron…
Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world. Prior to their widespread adoption, safety guarantees are needed on the controller behaviour that properly take account of the uncertainty within the mod…
Insurance contracts for autonomous AI agents must be actuarially sound and resistant to gaming.
Framework allows systems to defer difficult decisions to unknown experts.
Paper proposes methods to help autonomous vehicles adapt to unexpected driving scenarios.
In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active learning, have been successful in applications, on real-world systems they must provably avoid unsafe decisions. To this end, safe IML algo…
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in …
Safe learning of stochastic dynamics with safety constraints.
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confi…
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…
Paper defines ε-Safe Decision Regions for exponential family distributions and approximates them for unbalanced data.
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres…
We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings. We introduce a deep learning-based approach that takes into account a current world state and produces raster images of eac…
We enhance autonomous materials research with problem-aware models.
Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robo…
In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Mo…
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 solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic st…
PhysVarMix predicts diverse urban trajectories with physics constraints.
This work improves autonomous racing by creating diverse opponents and adapting risk.
Safe Gaussian Process Bandit Optimization with sub-linear regret bounds.
Develops a learning model predictive controller for competitive racing.
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be in…
A machine learning environment for detecting autonomous vehicle corner cases.
Bayesian inference models failure distributions in autonomous systems.
Algorithm finds safe zones in policy Markov Decision Processes to limit trajectory escape.
A novel approach for safe offline RL using latent safety constraints.
Safe Bayesian optimization method using information theory.
New method provides scalable safety guarantees for RL agents.
A new algorithm trains experts to safely guide agents in partially observed environments.
Proposes a new model for predicting future motion of road actors in autonomous vehicles.
FunCLBM clusters time series data for autonomous driving validation.
The paper extends conformal prediction to MDP trajectories for autonomous systems.
Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all po…
CoCoRL learns safe constraints from demonstrations with unknown rewards.
ARTEO algorithm optimizes safety-critical systems with uncertainty.