6 papers
Context-Aware Intelligent Vehicles
Liangkai Liu, Shuyao Shi, Mingke Wang +4
Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-cabin monitoring and fleet ma…
Adversarial Calibration Attack on Autonomous Vehicles
Liangkai Liu, Qingzhao Zhang, Kang G. Shin
Autonomous vehicles (AVs) rely on accurate camera-LiDAR calibration for multimodal sensor fusion. In practice, calibration can drift due to vibration, temperature variation, or min…
MM-BEV: Enhancing Timeliness by Computing Where and When it Matters
Liangkai Liu, Kang G. Shin
Multimodal bird's-eye-view (BEV) perception combines LiDAR depth accuracy with dense camera semantics, but its high computational cost and imperfect sensing conditions make real-ti…
Feeling the Space: Egomotion-Aware Video Representation for Efficient and Accurate 3D Scene Understanding
Shuyao Shi, Kang G. Shin
Recent Multimodal Large Language Models (MLLMs) have shown high potential for spatial reasoning within 3D scenes. However, they typically rely on computationally expensive 3D repre…
Enhancing Predictability of Multi-Tenant DNN Inference for Autonomous Vehicles' Perception
Liangkai Liu, Kang G. Shin, Jinkyu Lee +2
Autonomous vehicles (AVs) rely on sensors and deep neural networks (DNNs) to perceive their surrounding environment and make maneuver decisions in real time. However, achieving rea…
Power-Efficient Autonomous Mobile Robots
Liangkai Liu, Weisong Shi, Kang G. Shin
This paper presents pNav, a novel power-management system that significantly enhances the power/energy-efficiency of Autonomous Mobile Robots (AMRs) by jointly optimizing their phy…