14 citations · 14 across the 8 of their papers we have counts for
8 papers
A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios
Dong Bi, Yongqi Zhao, Paul Kovacevic +4
Personalized driving can improve the user acceptance of automated driving systems. However, existing methods still provide limited support for translating natural-language driving…
Automated Digital Twin Construction for Highway Scenarios Using LiDAR Point Clouds and OpenStreetMap
Yongqi Zhao, Dong Bi, Paul Kovacevic +4
Accurate road environment modeling is fundamental to the simulation and validation of automated driving systems. However, constructing road maps in standardized formats such as ASA…
A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions
Ji Zhou, Yilin Ding, Yongqi Zhao +4
Reliable environmental perception remains one of the main obstacles for safe operation of automated vehicles. Safety of the Intended Functionality (SOTIF) concerns safety risks fro…
A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods
Ji Zhou, Yongqi Zhao, Yixian Hu +4
Ensuring the safety and reliability of Automated Driving Systems (ADS) remains a critical challenge, as traditional verification methods such as large-scale on-road testing are pro…
Toward a Full-Stack Co-Simulation Platform for Testing of Automated Driving Systems
Dong Bi, Yongqi Zhao, Zhengguo Gu +3
Virtual testing has emerged as an effective approach to accelerate the deployment of automated driving systems. Nevertheless, existing simulation toolchains encounter difficulties…
A Communication-Latency-Aware Co-Simulation Platform for Safety and Comfort Evaluation of Cloud-Controlled ICVs
Yongqi Zhao, Xinrui Zhang, Tomislav Mihalj +10
Testing cloud-controlled intelligent connected vehicles (ICVs) requires simulation environments that faithfully emulate both vehicle behavior and realistic communication latencies.…
A Survey on the Application of Large Language Models in Scenario-Based Testing of Automated Driving Systems
Yongqi Zhao, Ji Zhou, Dong Bi +3
The safety and reliability of Automated Driving Systems (ADSs) must be validated prior to large-scale deployment. Among existing validation approaches, scenario-based testing has b…
Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model
Yongqi Zhao, Wenbo Xiao, Tomislav Mihalj +2
The advent of Large Language Models (LLM) provides new insights to validate Automated Driving Systems (ADS). In the herein-introduced work, a novel approach to extracting scenarios…