An On-the-Road Comparison of In-Vehicle Navigation Assistance Systems

We compared system performance and driver opinion of 3 in-vehicle navigation aids - two advanced traveler information systems (ATISs; Ali-Scout and TetraStar) and written instructions - when used on the road concurrently under identical conditions. Few drivers in the study had difficulty finding initial routes or became lost. Users of Ali-Scout, an ATIS that utilizes traffic information in routing, drove longer-distance routes, got lost more frequently, and gave their system less positive ratings than did TetraStar users. Users of the 2 ATISs traversed routes that were significantly shorter in duration than those driven by users of written instructions. The time savings benefit of the advanced technology systems over written instructions was greatest during peak traffic conditions. Drivers who were familiar with the road network, overall, had less difficulty finding destinations and drove shorter-duration routes than drivers who were unfamiliar with the road network. Actual or potential applications of this research include improving the design of technologies that provide navigation assistance to travelers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67382/2/10.1518_001872099779591222.pd

Road Map of Autonomous Vehicle Service Deployment Priorities in Ann Arbor

This report assesses the opportunities within Ann Arbor for the deployment of fully autonomous vehicles (AV) and provides recommendations on which AV systems can potentially enhance sustainability of personal transportation. Throughout the course of this research, we assume automated driving capabilities have met both technical and legal requirements to ensure safe and effective fully self-driving operation on public roads (NHTSA level 4, SAE level 5). We develop a multi-disciplinary research framework to understand the broader impacts of AV systems across various dimensions. This approach involves determining the travel demand for a proposed service based on trip behavior via existing modes, designing and optimizing an AV fleet according to the service’s demand profile and drive cycle, and evaluating the AV system, and its competing modes, across a set of sustainability and system performance metrics.http://deepblue.lib.umich.edu/bitstream/2027.42/191692/1/CSS16-21.pdfDescription of CSS16-21.pdf : ReportSEL