Sandeep Thalapanane — accepted AI-conference papers

2025

DISC: Dataset for Analyzing Driving Styles in Simulated Crashes for Mixed Autonomy

Sandip Sharan Senthil Kumar, Sandeep Thalapanane, Guru Nandhan Appiya Dilipkumar Peethambari, Sourang SriHari, Laura Zheng, Ming C. Lin

ICRA 2025

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 In Simulated Crashes), one of the first datasets designed to capture various driving styles and behaviors in precrash sce

Cited by 2 Source ↗Scholar ↗

2025

Quantifying and Modeling Driving Styles in Trajectory Forecasting

Laura Zheng, Hamidreza Yaghoubi Araghi, Tony Wu, Sandeep Thalapanane, Tianyi Zhou, Ming C. Lin

IROS 2025

Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, trajectory forecasting predicts the trajectory of a short-horizon future for specific human drivers in a particular traffic scenario. Robust and accurate

Cited by 2 Source ↗Scholar ↗

2024

TRAVERSE: Traffic-Responsive Autonomous Vehicle Experience & Rare-event Simulation for Enhanced safety

Sandeep Thalapanane, Sandip Sharan Senthil Kumar, Guru Nandhan Appiya Dilipkumar Peethambari, Sourang SriHari, Laura Zheng, Julio Poveda +1

IROS 2024poster

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 bias towards safe driving, making self-driving cars unprepared when encountering adversarial scenarios like unexpected acc…

Cited by 1 Source ↗Scholar ↗