In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While collecting sensor data of the vehicle's surroundings, we obtained driving commentary from a driving instructor using the think-aloud protocol. We analysed the driving commentary and uncovered an explanation style; the driver first announces his observations, announces his plans, and then makes general remarks. He also makes counterfactual comments. We successfully demonstrated how factual and counterfactual natural language explanations that follow this style could be automatically generated using a transparent tree-based approach. Generated explanations for longitudinal actions (e.g., stop and move) were deemed more intelligible and plausible by human judges compared to lateral actions, such as lane changes. We discussed how our approach can be built on in the future to realise more robust and effective explainability for driver assistance as well as partial and conditional automation of driving functions.
翻译:在评论驱动中,驾驶员口头说明他们的观察、评估和意图。通过讲述他们的思维,学习和专家驾驶员能够更好地了解和认识他们的周围环境。在智能车辆方面,自动驾驶评注可以提供对驾驶行动的清晰的解释,从而协助驾驶员或最终用户在驾驶作业中采取具有挑战性和安全批评性的情况。在本文件中,我们进行了一项实地研究,在城市环境中部署了一个研究工具以收集数据。在收集车辆周围的传感器数据时,我们从驾驶教员处获得了驾驶说明,我们分析了驾驶说明并发现了解释风格;驾驶员首先发表了他的意见,宣布了他的计划,然后作了一般性的评论。他还作了反事实的评论。我们成功地展示了如何用透明的基于树木的方法自动产生符合这种风格的事实和真实的自然语言解释。人们认为,与后来的行动相比,人类法官们对长度行动(例如停停和移动)的解释更能理解和可信。我们讨论了我们的方法如何能够在未来的汽车变换等灵活机动性方面建立起来。我们讨论了我们的方法如何成为更坚实和最可靠和最可靠的工具。