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

全球每年约有120万起交通事故，其中驾驶员行为或驾驶员失误是最关键的原因，人们普遍预计，提高车辆自主性将极大地减少驾驶员失误。世界各地的政府和行业都在认识到车辆自动化最终将带来的深远影响。加拿大参议院交通通信委员会2018年1月发布的一份报告提出了16项建议，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项新兴技术的影响。未来几十年将带来混合交通流量，人类驾驶和自动驾驶的车辆将共享道路。人类驾驶员和自动驾驶系统之间的这些复杂的相互作用(在同一车辆内或在不同车辆之间)具有极大的潜在诱导人驾驶错误和/或自动驾驶系统决策错误。这些挑战表明，尽管在过去几十年里在这一领域已经做出了相当大的努力，但对进一步加强对人类驾驶行为的理解和建模的需求正在显现。*本研究计划的长期目标是在各种驾驶场景下，包括正常驾驶场景和关键场景(或角落情况)下，提高人类驾驶员行为对认知自动驾驶的科学和理解。为了实现这一长期目标，这笔发现基金专注于开发和验证新的迁移学习框架，以适应驾驶员的行为模式。除了现有的公共数据集外，还将使用驾驶模拟器和真实车辆收集新的数据，用于模型训练和验证。所提出的研究方案和研究成果将有助于增强对人类驾驶行为(以及日常活动中的人类行为)的理解和建模，这对认知自主驾驶的新兴发展也非常有价值，因为认知自主驾驶需要自动车辆与周围车辆以及车内操作员的交互感知能力。作为另一个有价值的成果，在该计划内收集的数据集将被公布用于公共研究目的。*这项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，该项目的研究工作将有助于推动加拿大制造的具有更高智能性和安全性的未来一代自动驾驶汽车。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，并加快自动驾驶汽车在加拿大和世界各地的普及。**

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. **