9 citations · 18 across the 13 of their papers we have counts for
14 papers
Differentiable Dynamics for Autonomous Micro-Mobility Navigation
Grace Cai, Joey Lee, Nithin Parepally +2
Autonomous micro-mobility vehicles (MMVs) such as wheelchairs, scooters, and bicycles have the potential to improve mobility access and support safe low-speed transportation in ped…
GM3: A General Physical Model for Micro-Mobility Vehicles
Grace Cai, Nithin Parepally, Laura Zheng +1
Modeling the dynamics of micro-mobility vehicles (MMV) is becoming increasingly important for training autonomous vehicle systems and building urban traffic simulations. However, m…
Quantifying and Modeling Driving Styles in Trajectory Forecasting
Laura Zheng, Hamidreza Yaghoubi Araghi, Tony Wu +3
Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, trajectory forecasting predicts th…
DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomy
Sandip Sharan Senthil Kumar, Sandeep Thalapanane, Guru Nandhan Appiya Dilipkumar Peethambari +3
Handling pre-crash scenarios is still a major challenge for self-driving cars due to limited practical data and human-driving behavior datasets. We introduce DISC (Driving Styles I…
Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
Sanghyun Son, Laura Zheng, Brian Clipp +3
We present a parallelized differentiable traffic simulator based on the Intelligent Driver Model (IDM), a car-following framework that incorporates driver behavior as key variables…
TRAVERSE: Traffic-Responsive Autonomous Vehicle Experience & Rare-event Simulation for Enhanced safety
Sandeep Thalapanane, Sandip Sharan Senthil Kumar, Guru Nandhan Appiya Dilipkumar Peethambari +4
Data for training learning-enabled self-driving cars in the physical world are typically collected in a safe, normal environment. Such data distribution often engenders a strong bi…
Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic
Laura Zheng, Sanghyun Son, Jing Liang +3
In trajectory forecasting tasks for traffic, future output trajectories can be computed by advancing the ego vehicle's state with predicted actions according to a kinematics model.…
Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation
Laura Zheng, Wenjie Wei, Tony Wu +5
Achieving robustness in image segmentation models is challenging due to the fine-grained nature of pixel-level classification. These models, which are crucial for many real-time pe…
Gradient Informed Proximal Policy Optimization
Sanghyun Son, Laura Yu Zheng, Ryan Sullivan +2
We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm. To incorpor…
Towards Driving Policies with Personality: Modeling Behavior and Style in Risky Scenarios via Data Collection in Virtual Reality
Laura Zheng, Julio Poveda, James Mullen +2
Autonomous driving research currently faces data sparsity in representation of risky scenarios. Such data is both difficult to obtain ethically in the real world, and unreliable to…
Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation
Laura Zheng, Sanghyun Son, Ming C. Lin
While there have been advancements in autonomous driving control and traffic simulation, there have been little to no works exploring their unification with deep learning. Works in…
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving
Shivam Akhauri, Laura Zheng, Tom Goldstein +1
Practical learning-based autonomous driving models must be capable of generalizing learned behaviors from simulated to real domains, and from training data to unseen domains with u…
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
Yu Shen, Laura Zheng, Manli Shu +3
For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments. These external and environ…
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation
Shivam Akhauri, Laura Zheng, Ming Lin
Simulation data can be utilized to extend real-world driving data in order to cover edge cases, such as vehicle accidents. The importance of handling edge cases can be observed in…