Search Results for author:
Found 9 papers, 3 papers with code
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving
The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the driving behaviour from experts.
A hierarchical semantic segmentation framework for computer vision-based bridge damage detection
However, existing methods perform poorly when detecting small damages (e. g., cracks and exposed rebars) and thin objects with limited image samples, especially when the components of interest are highly imbalanced.
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing
In the first stage of the challenge, we evaluate an autonomous agent's ability to drive as fast as possible, while adhering to safety constraints.
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
Anomaly detection has many important applications, such as monitoring industrial equipment.
Safe Autonomous Racing via Approximate Reachability on Ego-vision
Given the nature of the task, autonomous agents need to be able to 1) identify and avoid unsafe scenarios under the complex vehicle dynamics, and 2) make sub-second decision in a fast-changing environment.
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization
Specifically, we incorporate a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible.
Learn-to-Race: A Multimodal Control Environment for Autonomous Racing
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing.
Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings
In this paper, we show efficient strategies that circumvent this problem by differentiating through the operations of a power flow solver that embeds the power flow equations into a holomorphic function.
Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.
