| 摘 要： |
Effective driving style analysis is critical to developing human-centered intelligent driving systems that consider drivers' preferences. However, the approaches and conclusions of most related studies are diverse and inconsistent because no unified datasets tagged with driving styles exist as a reliable benchmark. The absence of explicit driving style labels makes verifying different approaches and algorithms difficult. This paper provides a new benchmark by constructing a natural dataset of Driving Style (100-DrivingStyle) tagged with the subjective evaluation of 100 drivers' driving styles. In this dataset, the subjective quantification of each driver's driving style is from themselves and an expert according to the Likert-scale questionnaire. The testing routes are selected to cover various driving scenarios, including highways, urban, highway ramps, and signalized traffic. The collected driving data consists of lateral and longitudinal manipulation information, including steering angle, steering speed, lateral acceleration, throttle position, throttle rate, brake pressure, etc. This dataset is the first to provide detailed manipulation data with driving-style tags, and we demonstrate its benchmark function using six classifiers. The 100-DrivingStyle dataset is available via https://github.com/chaopengzhang/100-DrivingStyle-Dataset.
有效的驾驶风格分析对于开发以人为本的智能驾驶系统至关重要，此类系统需考虑驾驶员的偏好。然而，由于目前尚无统一标注驾驶风格的可靠数据集作为基准，大多数相关研究的方法和结论存在差异且不一致。缺乏明确的驾驶风格标签使得验证不同方法和算法变得困难。本文通过构建一个包含100名驾驶员主观评价驾驶风格的自然数据集（Driving Style 100-DrivingStyle），提供了一种新的基准。该数据集中，每位驾驶员的驾驶风格主观量化由其本人及专家根据李克特量表问卷进行评估。测试路线涵盖多种驾驶场景，包括高速公路、城市道路、高速公路匝道以及有信号灯的交通环境。收集的驾驶数据包括横向与纵向操控信息，如转向角度、转向速度、横向加速度、油门位置、油门响应率、刹车压力等。该数据集是首个提供带驾驶风格标签的详细操作数据，并通过六个分类器展示了其基准性能。100-DrivingStyle 数据集可通过 https://github.com/chaopengzhang/100-DrivingStyle-Dataset 获取。 |
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