Search Results for author: Bingqing Chen

Found 10 papers, 4 papers with code

From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers

1 code implementation CVPR 2024 Swaminathan Gurumurthy, Karnik Ram, Bingqing Chen, Zachary Manchester, Zico Kolter

We then propose a simple, yet effective solution to reduce the gradient variance by using the weights predicted by the network in the inner optimization loop to weight the correspondence objective in the training problem.

Pose Estimation Visual Odometry

Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving

no code implementations16 Dec 2022 Jonathan Francis, Bingqing Chen, Weiran Yao, Eric Nyberg, Jean Oh

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.

Autonomous Driving Density Estimation +1

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

1 code implementation18 Jul 2022 Jingxiao Liu, Yujie Wei, Bingqing Chen

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.

Image Segmentation Semantic Segmentation

Safe Autonomous Racing via Approximate Reachability on Ego-vision

no code implementations14 Oct 2021 Bingqing Chen, Jonathan Francis, Jean Oh, Eric Nyberg, Sylvia L. Herbert

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.

Autonomous Driving Reinforcement Learning (RL) +1

Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization

1 code implementation19 May 2021 Bingqing Chen, Priya Donti, Kyri Baker, J. Zico Kolter, Mario Berges

Specifically, we incorporate a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible.

Reinforcement Learning (RL)

Learn-to-Race: A Multimodal Control Environment for Autonomous Racing

1 code implementation ICCV 2021 James Herman, Jonathan Francis, Siddha Ganju, Bingqing Chen, Anirudh Koul, Abhinav Gupta, Alexey Skabelkin, Ivan Zhukov, Max Kumskoy, Eric Nyberg

Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing.

Autonomous Driving Trajectory Prediction

Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings

no code implementations16 Dec 2020 Henning Lange, Bingqing Chen, Mario Berges, Soummya Kar

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

no code implementations6 Feb 2020 Jingxiao Liu, Bingqing Chen, Siheng Chen, Mario Berges, Jacobo Bielak, HaeYoung Noh

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.

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