Current technologies are unable to produce massively deployable, fully autonomous vehicles that do not require human intervention. Such technological limitations are projected to persist for decades. Therefore, roadway scenarios requiring a driver to regain control of a vehicle, and vice versa, will remain critical to the safe operation of semi-autonomous vehicles for the foreseeable future. Herein, we adopt a comprehensive perspective on this problem taking into account the operational design domain, driver and environment monitoring, trajectory planning, and driver intervention performance assessment. Leveraging decision analysis and Bayesian forecasting, both the support of driving mode management decisions and the issuing of early warnings to the driver are addressed. A statistical modeling framework is created and a suite of algorithms are developed to manage driving modes and issue relevant warnings in accordance with the management by exception principle. The efficacy of these developed methods are then illustrated and examined via a simulated case study.

翻译：目前的技术无法产生不需要人力干预的大规模可部署、完全自主的车辆,这种技术限制预计会持续几十年,因此,要求司机重新控制车辆的公路设想方案对于半自主车辆在可预见的将来的安全运行仍然至关重要,因此,我们对这个问题采取全面的观点,同时考虑到操作设计领域、驾驶员和环境监测、轨迹规划和驾驶员干预绩效评估,利用决策分析和巴耶斯预报,既支持驾驶模式管理决定,又向驾驶员发出预警,因此,建立一个统计模型框架,并开发一套算法,以管理驾驶模式,并根据例外管理原则发出相关警告,然后通过模拟案例研究来说明和审查这些已开发方法的功效。