Supplementary data for the paper 'What makes a good driver on public roads and race tracks? An interview study'

Future vehicles may drive automatically in a human-like manner or contain systems that monitor human driving ability. Algorithms of these systems must have knowledge of criteria of good and safe driving behavior with regard to different driving styles. In the current study, interviews were conducted with 30 drivers, including driving instructors, engineering, and race drivers. The participants were asked to describe good driving on public roads and race tracks, and in some questions were supported with video material. The results were interpreted with the help of Endsley’s model of situation awareness. The interviews showed that there were clear differences between what was considered good driving on the race track and good driving on the public road, where for the former, the driver must touch the limit of the vehicle, whereas, for the latter, the limit should be avoided. However, in both cases, a good driver was characterized by self-confidence, lack of stress, and not being aggressive. Furthermore, it was mentioned that the driver’s posture and viewing behavior are essential components of good driving, which affect the driver’s prediction of events and execution of maneuvers. The implications of our findings for the development of automation technology are discussed. In particular, we see potential in driver posture estimation and argue that automated vehicles excel in perception but may have difficulty making predictions.<br>

未来车辆可采用类人化方式实现自动驾驶，或搭载用于监测人类驾驶能力的系统。此类系统的算法需通晓针对不同驾驶风格的优质安全驾驶行为准则。本研究共访谈30名驾驶员，涵盖驾驶教练员、工程从业者与赛车手。研究要求参与者分别描述公共道路与赛车赛道上的良好驾驶行为，部分问题辅以视频素材辅助说明。本研究借助恩兹利情境意识模型（Endsley's model of situation awareness）对访谈结果进行解读。访谈结果表明，公众对于赛道与公共道路的良好驾驶行为认知存在显著分野：赛道驾驶中驾驶员需充分触及车辆性能极限，而公共道路驾驶则应刻意规避触碰该极限。但两类场景下，优秀驾驶员的共性特征均为自信沉稳、心态平和且无攻击性驾驶行为。此外，受访驾驶员提及，驾驶姿势与视线行为是良好驾驶的核心构成要素，二者会影响驾驶员对路况事件的预判及操作动作的执行效果。本文探讨了本研究发现对自动驾驶技术开发的启示意义，特别指出驾驶员姿势估计领域具备应用潜力，并提出：自动驾驶车辆虽在感知层面表现卓越，但在预测环节可能存在明显短板。