2 citations · 5 across the 17 of their papers we have counts for
18 papers
TEACar: An Open-Source Autonomous Driving Platform
Zhongzheng Zhang, Maxwell Ruyle, Andrew Kappes +5
Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms that support realistic hardwa…
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2
Reliable confidence estimates are important for safely deploying vision-based controllers in autonomous racing, where safety predictions must be derived from camera images, yet mod…
Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning
Zhenjiang Mao, Anirudhh Venkat, Artem Bisliouk +4
Large Language Models (LLMs) increasingly rely on long-form, multi-step reasoning to solve complex tasks such as mathematical problem solving and scientific question answering. Des…
Deterministic World Models for Closed-loop Reachability Analysis of End-to-End Vision-based Control
Yuang Geng, Zhongzheng Zhang, Chengzhen Jiang +5
End-to-end image controllers that map raw camera frames directly to control actions are increasingly deployed in safety-critical systems. However, formally verifying their closed-l…
Statistical-Symbolic Verification of Perception-Based Autonomous Systems using State-Dependent Conformal Prediction
Yuang Geng, Thomas Waite, Trevor Turnquist +2
Reachability analysis has been a prominent way to provide safety guarantees for neurally controlled autonomous systems, but its direct application to neural perception components i…
Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing
Christopher Oeltjen, Carson Sobolewski, Saleh Faghfoorian +4
Accurate knowledge of the tire-road friction coefficient (TRFC) is essential for vehicle safety, stability, and performance, especially in autonomous racing, where vehicles often o…
Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy
Jordan Peper, Yan Miao, Sayan Mitra +1
Precise and comprehensive situational awareness is a critical capability of modern autonomous systems. Deep neural networks that perceive task-critical details from rich sensory si…
How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Autonomous robots that rely on deep neural network controllers pose critical challenges for safety prediction, especially under partial observability and distribution shift. Tradit…
Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic
Zhenjiang Mao, Artem Bisliouk, Rohith Reddy Nama +1
Large Language Models (LLMs) have shown impressive performance in mathematical reasoning tasks when guided by Chain-of-Thought (CoT) prompting. However, they tend to produce highly…
Generalizable Image Repair for Robust Visual Control
Carson Sobolewski, Zhenjiang Mao, Kshitij Maruti Vejre +1
Vision-based control relies on accurate perception to achieve robustness. However, image distribution changes caused by sensor noise, adverse weather, and dynamic lighting can degr…
Four Principles for Physically Interpretable World Models
Jordan Peper, Zhenjiang Mao, Yuang Geng +2
As autonomous systems are increasingly deployed in open and uncertain settings, there is a growing need for trustworthy world models that can reliably predict future high-dimension…
State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems
Thomas Waite, Yuang Geng, Trevor Turnquist +2
It remains a challenge to provide safety guarantees for autonomous systems with neural perception and control. A typical approach obtains symbolic bounds on perception error (e.g.,…
Physically Interpretable World Models via Weakly Supervised Representation Learning
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Learning predictive models from high-dimensional sensory observations is fundamental for cyber-physical systems, yet the latent representations learned by standard world models lac…
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving
Zhenjiang Mao, Dong-You Jhong, Ao Wang +1
Out-of-distribution (OOD) detection is essential in autonomous driving, to determine when learning-based components encounter unexpected inputs. Traditional detectors typically use…
Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models
Zhenjiang Mao, Siqi Dai, Yuang Geng +1
A world model creates a surrogate world to train a controller and predict safety violations by learning the internal dynamic model of systems. However, the existing world models re…
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
Yuang Geng, Jake Brandon Baldauf, Souradeep Dutta +2
Autonomous systems are increasingly implemented using end-to-end learning-based controllers. Such controllers make decisions that are executed on the real system, with images as on…
How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Carson Sobolewski, Ivan Ruchkin
End-to-end learning has emerged as a major paradigm for developing autonomous systems. Unfortunately, with its performance and convenience comes an even greater challenge of safety…
Single-window Integrated Development Environment
Ivan Ruchkin, Vladimir Prus
This paper addresses the problem of IDE interface complexity by introducing single-window graphical user interface. This approach lies in removing additional child windows from IDE…