1 citations · 1 across the 2 of their papers we have counts for
5 papers
RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution
Lanxin Xiang, Liang Shi, Youhui Ye +3
Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attribution measures often exhibit…
Perception Characteristics Distance: Measuring Stability and Robustness of Perception System in Dynamic Conditions under a Certain Decision Rule
Boyu Jiang, Liang Shi, Zhengzhi Lin +3
The safety of autonomous driving systems (ADS) depends on accurate perception across distance and driving conditions. The outputs of AI perception algorithms are stochastic, which…
SynSHRP2: A Synthetic Multimodal Benchmark for Driving Safety-critical Events Derived from Real-world Driving Data
Liang Shi, Boyu Jiang, Zhenyuan Yuan +2
Driving-related safety-critical events (SCEs), including crashes and near-crashes, provide essential insights for the development and safety evaluation of automated driving systems…
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding
Tong Zeng, Longfeng Wu, Liang Shi +2
Vision Large Language Models (VLLMs) have demonstrated impressive capabilities in general visual tasks such as image captioning and visual question answering. However, their effect…
ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
Liang Shi, Boyu Jiang, Tong Zeng +1
Accurately identifying, understanding and describing traffic safety-critical events (SCEs), including crashes, tire strikes, and near-crashes, is crucial for advanced driver assist…