Language and Ergonomics (CLLE) Laboratory, Cognition, Languages, University of Toulouse-Jean Jaurès, Toulouse, France.
Toulouse Computer Science Research Institute (IRIT), Paul Sabatier University, Toulouse, France.
PLoS One. 2023 Feb 9;18(2):e0281702. doi: 10.1371/journal.pone.0281702. eCollection 2023.
Studies investigating the question of how automated cars (ACs) should drive converge to show that a personalized automated driving-style, i.e., mimicking the driving-style of the human behind the wheel, has a positive influence on various aspects of his experience (e.g., comfort). However, few studies have investigated the fact that these benefits might vary with respect to driver-related variables, such as trust in ACs, and contextual variables of the driving activity, such as weather conditions. Additionally, the context of intermediate levels of automation, such as SAE level 3, remains largely unexplored. The objective of this study was to investigate these points. In a scenario-based experimental protocol, participants were exposed to written scenarios in which a character is driven by a SAE level 3 AC in different combinations of conditions (i.e., types of roads, weather conditions and traffic congestion levels). For each condition, participants were asked to indicate how fast they would prefer their AC to drive and how fast they would manually drive in the same situation. Through analyses of variance and equivalence tests, results showed a tendency for participants to overall prefer a slightly lower AC speed than their own. However, a linear regression analysis showed that while participants with the lowest levels of trust preferred an AC speed lower than theirs, those with the highest levels preferred an AC speed nearly identical to theirs. Overall, the results of this study suggest that it would be more beneficial to implement a personalization approach for the design of automated driving-styles rather than a one for all approach.
研究自动驾驶汽车（AC）应如何驾驶的问题已经达成共识，即模仿人类驾驶员的驾驶风格的个性化自动驾驶风格对其体验的各个方面（如舒适度）都有积极的影响。然而，很少有研究调查这些好处可能因驾驶员相关变量（如对 AC 的信任）和驾驶活动的情境变量（如天气条件）而异。此外，中间级别的自动化环境，如 SAE 级别 3，仍然在很大程度上没有被探索。本研究的目的是调查这些问题。在基于场景的实验方案中，参与者接触到书面场景，其中一个角色在不同的条件组合（即道路类型、天气条件和交通拥堵水平）下由 SAE 级别 3 AC 驾驶。对于每种情况，参与者被要求表明他们希望 AC 以多快的速度行驶，以及在相同情况下他们自己手动驾驶的速度。通过方差分析和等价性检验，结果表明参与者总体上倾向于比自己的速度稍慢的 AC 速度。然而，线性回归分析表明，尽管信任度最低的参与者更喜欢比自己的速度低的 AC 速度，但信任度最高的参与者更喜欢与自己的速度几乎相同的 AC 速度。总的来说，这项研究的结果表明，对于自动驾驶风格的设计，实施个性化方法会比一刀切的方法更有益。
