LLM-Driven Testing for Autonomous Driving Scenarios

Engineering notes

As outcome, we implement a prototype and evaluate the proposed approach on autonomous driving feature scenarios in CARLA open-source simulation environment. According to the achieved results, GPT-4 outperforms Llama3, while the presented approach speeds-up the process of testing (more than 10 times) and reduces cognitive load thanks to automated code generation and adoption of flexible simulation environment for quick evaluation.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

In this paper, we explore the potential of leveraging Large Language Models (LLMs) for automated test generation based on free-form textual descriptions in area of automotive. As outcome, we implement a prototype and evaluate the proposed approach on autonomous driving feature scenarios in CARLA open-source simulation environment. Two pre-trained LLMs are taken into account for comparative evaluation: GPT-4 and Llama3. According to the achieved results, GPT-4 outperforms Llama3, while the presented approach speeds-up the process of testing (more than 10 times) and reduces cognitive load thanks to automated code generation and adoption of flexible simulation environment for quick evaluation.

Links and sources

• Official / arXiv page

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