14 citations · 15 across the 11 of their papers we have counts for
11 papers
Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles
Duc-Tien Bui, Ngoc Thinh Nguyen, Hung Duy Nguyen +3
Path planning is a fundamental component of autonomous vehicles, where achieving safe, comfortable, and dynamically feasible paths while ensuring computational efficiency remains a…
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…
Lane Change Intention Prediction of two distinct Populations using a Transformer
Francesco De Cristofaro, Cornelia Lex, Jia Hu +1
In complex traffic scenarios, intention prediction of surrounding vehicles can improve the strategy of automated driving functions. Existing work on intention prediction is often t…
Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer
Francesco De Cristofaro, Felix Hofbaur, Aixi Yang +1
Lane changes of preceding vehicles have a great impact on the motion planning of automated vehicles especially in complex traffic situations. Predicting them would benefit the publ…
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…
Human-Machine Shared Control Approach for the Takeover of CACC
Haoran Wang, Zhexi Lian, Zhenning Li +5
Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the…
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…