Transfer-learning-enabled driver behavior model adaptation towards cognitive autonomous driving

全球每年约有120万起道路死亡事故，其中驾驶员行为或驾驶员错误是最关键的原因，人们普遍预计，提高车辆自主性将大大减少驾驶员错误。世界各地的政府和行业都认识到汽车自动化最终将带来的深远影响。加拿大参议院交通和通信委员会于2018年1月发布的一份报告提出了16项建议，以帮助加拿大人为自动驾驶汽车做好准备，强调加拿大必须更好地规划这一新兴技术的影响。未来几十年将带来混合交通流，人类驾驶和自动驾驶车辆共享道路。人类驾驶员和自动驾驶系统之间（在同一车辆内或不同车辆之间）的这些复杂交互具有诱发人类驾驶员错误和/或自动驾驶系统决策错误的显著潜力。这些挑战说明了对进一步增强人类驾驶员行为的理解和建模的新兴需求，尽管在过去的几十年中在这一领域已经做出了相当大的努力。** 本研究计划的长期目标是在各种驾驶场景下，包括正常驾驶场景以及关键场景（或角落情况），增强对人类驾驶员行为的科学和理解，以实现认知自动驾驶。为了实现这一长期目标，这项发现资助的重点是开发和验证一个新的迁移学习框架，用于驾驶员行为模型的适应。除了可用的公共数据集外，还将使用驾驶模拟器和真实的车辆收集新数据，用于模型训练和验证。拟议的研究计划沿着研究成果将大大有助于增强对人类驾驶员行为（以及日常活动中的人类行为）的理解和建模，这对于认知自动驾驶的新兴发展也非常有价值，其中需要自动驾驶车辆与周围车辆以及内部操作员的交互感知能力。作为一个额外的有价值的成果，该计划中收集的数据集将被公布用于公共研究目的。** 该研究计划将对加拿大以自动驾驶为导向的公司或初创公司产生非常有价值的影响。由于加拿大公司将是这些技术的主要接受者，因此该计划的研究工作将有助于推动未来一代加拿大制造的自动驾驶汽车，提高智能和安全性。此外，通过传播，该研究项目的成果将有助于提高公众的接受度，为政策制定者提供与人类驾驶行为相关的建议，并加速自动驾驶汽车在加拿大和全球的渗透。**

There are about 1.2 million road fatalities worldwide every year in which driver behavior or driver error is the most critical causal factor, and it is widely expected that increasing vehicle autonomy will drastically reduce driver error. Governments and industry around the world are recognizing the far-reaching impacts that will eventually be delivered from vehicle automation. A Canadian Senate Committee on Transport and Communications report, published in Jan 2018, gave 16 recommendations to help prepare Canadians for autonomous vehicles, highlighting that Canada must plan better for the impacts of this emerging technology. The coming decades will bring mixed traffic flows, with human-driven and automated vehicles sharing the road. These complex interactions between human drivers and autonomous driving systems (within the same vehicle or among different vehicles) have significant potential to induce human driver errors and/or autonomous driving system decision errors. Such challenges illustrate the emerging need for further enhanced understanding and modeling of human driver behaviors, though considerable efforts have been made in this field in the past few decades. ******The long-term goal of this research program is to enhance the science and understanding in human driver behaviors towards cognitive autonomous driving, under various driving scenarios, which include normal driving scenarios as well as critical scenarios (or corner cases). Towards achieving this long-term goal, this discovery grant focuses on development and validation of a new transfer learning framework for driver behavior model adaptation. Apart from the available public dataset, new data will be collected using driving simulator and real vehicles, for model training and validation. The proposed research program along with the research outcomes will contribute considerably to the enhanced understanding and modeling of human driver behaviors (and also human behaviors in daily activities), which is also very valuable for the emerging development of cognitive autonomous driving, where the interaction-awareness capacity of autonomous vehicles is needed with surrounding vehicles as well as the operator inside. As an additional valuable outcome, the datasets collected within this program will be published for public research purposes. ******This research program will have a very valuable impact in automated-driving-oriented companies or startups in Canada. Since Canadian companies will be the primary receptors of these technologies, the research efforts in this program will contribute to advancing future-generation Canadian-made automated driving vehicles with enhanced intelligence and safety. Furthermore, through disseminations, the outcomes of this research program will assist in boosting the public acceptance, offer recommendations related to human driver behaviors for policy makers, and accelerate the penetration of automated driving vehicles in Canada and worldwide. **