Human drivers are requested by the Automated Vehicle (AV) to perform takeover actions if needed. Existing research mainly focuses on predicting the takeover quality due to distraction using wearable sensor data. It is unrealistic, unnatural, and inapplicable to require human drivers to wear these sensors when driving an AV so that their situational awareness for takeover actions can be continuously monitored. Moreover, traffic conflicts can be observed even if drivers take over as requested. Current practice mainly develops conflict-actuated collision avoidance systems that alert drivers once the traffic conflict reaches a certain threshold. There is a research need to anticipate conflicts other than by measuring them. Besides, drivers are still responsible for responding to the alerts, which leaves the possibility of resulting in human error-related safety issues. This research aims at developing a Non-intrusive, Ultra-advanced Collision Avoidance System (NIUCAS) under automated driving. NIUCAS applies the brake pedal for drivers if it predicts the absence of takeover actions due to distraction or predicts traffic conflicts before they can be measured. The NIUCAS prototype was implemented in a driving simulator. An experiment was conducted by recruiting sixty participants to drive a vehicle under Level 3 automation, going through jaywalking scenarios, and being requested to take over. Participants’ demographics were collected to predict the takeover actions, while vehicle-related performance was collected to predict the traffic conflicts. Three machine learning-based modeling techniques were chosen as candidates for predictions. Additionally, an empirical equation is formulated to quantify the safety benefits of implementing NIUCAS.

Empirical studies have demonstrated the existence of a well-defined Network Macroscopic Fundamental Diagram (NMFD). Well-shaped NMFD, however, may not be a universal law because urban networks often display heterogeneous congestion distributions. Therefore, different algorithms have been proposed to partition a heterogeneous network into homogeneous sub-networks. These algorithms mainly consider uni-modal networks, despite the multi-modal nature of urban traffic flows. This paper is one of the first studies dedicated to the partitioning problem of bi-modal networks. First, a three-step partitioning algorithm is developed concerning NMFD-based applications. Second, the role played by mode differentiation in bi-modal network partitioning is investigated through the lens of speed-NMFDs. The results demonstrate that the algorithm performs well. Mode differentiation should be considered in the partitioning process while is defined differently in free-flow and saturated conditions. The links' lengths, bus stop distribution, speed limit zone, and the existence of bus terminals might affect speed-NMFD properties.