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In this study, flipped instruction in an undergraduate engineering course in the ‘COVID’ online, remote environment was conducted and compared to onsite flipped instruction (i.e. pre-COVID) to explore potential changes in student perceptions. Student perceptions were gathered via survey instruments and investigated further through instructor interviews. This analysis was done at three universities and made possible by extensive research with the flipped classroom at these three schools as part of a previous NSF-funded study between 2014 and 2016. Results gathered in the online remote setting suggest positive changes in student perceptions of flipped instruction compared to the onsite environment, including the decreased perception of the ‘load’ imposed by the flipped classroom and the ‘effort‘’ required. Some desirable outcomes remained unchanged in the remote setting. The recent and emerging literature has suggested the remote, online environment dictated by the pandemic may be beneficial for flipped teaching and learning. These and other findings from conducting flipped classrooms at three engineering schools in the online environment are presented, including perceptions of the classroom environment (via the College and University Environment Inventory), benefits and drawbacks identified, student motivation levels, and perceived learning. 

As vehicles become autonomous and connected, intelligent management techniques can be utilized to operate an intersection without a traffic light. When a Connected Autonomous Vehicle (CAV) approaches an intersection, it shares its status and intended direction with the Intersection Manager (IM), and the IM checks the status of other CAVs and assigns a target velocity/reference trajectory for it to maintain. In practice, however, there is an unknown delay between the time a CAV sends a request to the IM and the moment it receives back the response, namely, the Round-Trip Delay (RTD). As a result, the CAV will start tracking the target velocity/reference trajectory later than when the IM expects, which may lead to accidents. In this article, we present a time-aware approach, Crossroads+, that makes CAVs’ behaviors deterministic despite the existence of the unknown RTD. In Crossroads+, we use timestamping and synchronization to ensure that both the IM and the CAVs have the same notion of time. The IM will also set a fixed start time to track the target velocity/reference trajectory for each CAV. The effectiveness of the proposed Crossroads+ technique is illustrated by experiments on a 1/10 scale model of an intersection with CAVs. We also built a simulator to demonstrate the scalability of Crossroads+ for multi-lane intersections. Results from our experiments indicate that our approach can reduce the position uncertainty by 15% in comparison with conventional techniques and achieve up to 36% better throughputs.