The SAE Level 2 General Motors Super Cruise system allows hands-free driving on system-compatible roads, combining Adaptive Cruise Control (ACC) and lane centering functionality. The system uses a face camera and a series of escalating alert stages to prompt the driver to pay close attention to driving and respond to takeover requests (TORs). This paper uses telematics-based data from over 2.8 million miles of Super Cruise engaged driving observed on 4,183 Model Year 2021 Cadillac Escalades during a 10-month data collection period. Telematics data was captured during ignition cycles, including GPS information, hard braking events, Advanced Automatic Collision Notification (AACN) events, and Super Cruise state changes. Approximately 89.8% of driver attention reminders were resolved without further escalating alerts. Drivers successfully resumed control without further escalation in 97.9% of TORs. In 0.01% of TORs, the drivers did not respond before the system brought the vehicle to a stop, with 79% occurring at speeds of 25 mph or less, and none above 45 mph. Consistent with an earlier telematics study examining early production Model Year 2018-2019 Super Cruise-equipped sedans, results indicated (1) Super Cruise driving (compared to a combined set of manual and Adaptive Cruise Control (ACC) driving) had a reduced incidence of braking events more severe than -2.6 m/s2, (2) disengagement type influenced the direction of shifts in braking profiles, and (3) zero AACN events were observed during Super Cruise engaged driving. This study illustrates that telematics-based methods, which collect large-scale data from geographically dispersed drivers using their own vehicles as they normally do, can enhance understanding of drivers' interactions with the ADAS systems. Relative to on-board data acquisition system studies, the telematics-based approach is particularly well-suited for examining infrequent safety-related events and for providing early insights into ADAS effectiveness.

Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user expertise and complex inconvenient tutorials for customized scenario creation. Simulation of Urban Mobility (SUMO) simulator, which has been presented as an open-source traffic simulation platform, has found use as an AV simulator but suffers from similar issues which makes it difficult for entry-level practitioners to utilize the simulator without significant time investment. In that regard, we provide two enhancements to SUMO simulator geared towards massively improving user experience and providing real-life like variability for surrounding traffic. Firstly, we calibrate a car-following model, Intelligent Driver Model (IDM), for highway and urban naturalistic driving data and sample randomly from the parameter distributions to create realistic background vehicle driving behavior. Secondly, we combine SUMO with OpenAI gym, creating a Python package placed in a docker container which can run simulations based on real world highway and urban layouts with generic output observations and input actions that can be processed via any AV pipeline. For the calibration, we provide results using simulated and real-life data. For the Sumo-Gym package, we showcase a simple AV platform which runs IDM and lane change throughout the highway loop and provide some qualitative results. Our aim through these enhancements is to provide an easy-to-use simulation environment which can be installed in any operating platform and can be readily used for AV testing and validation.

As people age, some of the commonly experienced psychomotor, visual, and cognitive declines can interfere with the ability to safely drive, often leading to situational avoidance of challenging driving situations. The effect of hearing impairment on these avoidance behaviors has not been comprehensively studied. Data from the American Automobile Association (AAA) Longitudinal Research on Aging Drivers (LongROAD) study were used to assess the effect of hearing impairment on driving avoidance, using three measures of hearing. Results indicated that hearing loss plays a complex role in driving avoidance, and that an objective hearing measure was a stronger predictor than hearing aid use and self-rated hearing. Greater hearing impairment was related to less nighttime and freeway driving, more trips farther than 15 mi from home, and lower odds of avoiding peak driving times. The moderating influence of hearing on both vision and cognition is also discussed, along with study implications and future research.

Studies over the past two decades have attempted to document and understand factors related to crashes involving older drivers to develop more effective countermeasures to reduce the frequency and severity of these crashes. Studies in which vehicle acceleration data can be recorded have begun to explore the relationship between rapid deceleration events (RDEs) and functional abilities among older drivers as a surrogate measure of unsafe driving. Recent naturalistic driving studies with older adults have found differing results using different thresholds to define an RDE. The present study examined the relationship among RDE rates, demographics, visual abilities, cognitive abilities, and driving comfort among a large cohort of older drivers, using two definitions of RDEs longitudinal deceleration of 0.35 g or greater (RDE35) and longitudinal deceleration of 0.75 g or greater (RDE75). The study utilized objective driving, objective functioning, and reported driving comfort data from 2774 participants of the multi -site AAA Longitudinal Research on Aging Drivers (LongROAD) study. RDE rates for each threshold were calculated per 1000 miles driven. Multivariate regression models with backward elimination were developed to examine how outcome measures were related to RDE rates. Too few RDE75 events were found for meaningful analysis. RDE35 rates were significantly associated with several covariates. RDE35 rates were related to declining functional abilities, but many other factors also played a significant role in the rate of RDE35s among older drivers, diminishing the value of using RDE35 rates as a surrogate measure of driving safety. In addition, because the AAA LongROAD sample was relatively healthy and high functioning, other ability-related covariates may also be significantly related to RDE35s but the lack of variance in these measures in the current study prevented these effects from emerging. (C) 2019 Elsevier Ltd. All rights reserved.

This research brief used data from the AAA Longitudinal Research on Aging Drivers (LongROAD) study to examine the role of driving comfort in the self-regulation of driving by older adults. Self-regulation is the process by which individuals modify or adjust their driving patterns by driving less, or intentionally avoiding situations considered challenging. The process of self-regulation is complex and myriad individual factors influence it, including age, sex and perceived driving-related abilities (Molnar et al., 2018). However, one of the most consistent findings in the literature has been that drivers’ confidence — referred to here as comfort — in specific driving situations are closely related to their likelihood to self-regulate their driving (Molnar et al., 2015).

The exploratory study reported here was intended to examine: how strongly subjectively reported driving avoidance behaviors (commonly referred to as self-regulation) and exposure were related to their objectively measured counterparts and whether it depended on the specific behavior; the extent to which gender and age play a role in the association between subjectively reported driving avoidance behaviors and exposure and their objectively measured counterparts; and the extent to which demographics, health and functioning, driving-related perceptions, and cognition influence the association between subjective and objective driving avoidance behaviors overall. The study used data from the Longitudinal Research on Aging Drivers (LongROAD) study, a multisite, prospective cohort study designed to generate empirical data for understanding the role of medical, behavioral, environmental, and technological factors in driving safety during the process of aging. Objective driving measures were derived from GPS/datalogger data from 2131 LongROAD participants' vehicles. The corresponding subjective measures came from a comprehensive questionnaire administered to participants at baseline that asked them to report on their driving exposure, patterns, and other aspects of driving. Several other variables used in the analyses came from the comprehensive questionnaire and an inperson clinical assessment administered to participants at baseline. A series of simple linear and logistic models were fitted to examine the relationship between the subjective and objective driving measures of interest, and a multivariable analysis was conducted to examine the potential role of selected factors in the relationship between objective and subjective driving avoidance behaviors. Results of the models are presented and overall findings are discussed within the context of the existing research literature.

The objective of the study was to determine if there is a relationship between objective measures of visual function and objective measures of driving habits. The study used data from 2,131 drivers aged 65-79 enrolled in the United States based Longitudinal Research on Aging Drivers (LongROAD) study. Correlational analysis were conducted of three measures of visual function at baseline and six GPS-derived measures of driving averaged over the subsequent year. Results showed that participants had generally good visual function at the time of their enrollment. Analyses found that lower visual acuity and poorer visual perception abilities were related to a smaller driving space, lower driving exposure, and greater driving avoidance, although not for every measure. Poorer contrast sensitivity was associated with avoidance of nighttime driving and driving on high-speed roads, but was not related to driving space or exposure. This study provides evidence about how poor visual abilities can impact subsequent yearly driving. These results support other research evidence that the lower than expected crash-involvement of people with declining visual function may be related to the fact these drivers self-regulate their driving. A limitation of the study was that all significant correlations were relatively small, suggesting that other variables in addition to the ones analyzed may also be important for understanding the relationship between driving habits and visual function scores.

ABSTRACT Objective: The aging of the population in the United States and elsewhere has brought increasing attention to the issue of safe driving and mobility among older adults. The overall objective of this research was to use naturalistic data collection to better understand driving exposure and driving patterns, 2 important contributors to crash risk. Methods: Data came from a study conducted at the University of Michigan Transportation Research Institute as part of the Integrated Vehicle-Based Safety System (IVBSS) program. A total of 108 randomly sampled drivers took part, with the sample stratified by age and sex. The age groups examined were 20 to 30 (younger), 40 to 50 (middle-aged), and 60 to 70 years old (older). Sixteen late-model Honda Accords were used as research vehicles and were driven by participants as their personal vehicles over the study period. Roughly the first 2 weeks of vehicle use comprised the baseline driving period, during which the IVBSS technologies were turned off (i.e., no warnings were presented to the drivers) but all onboard data were collected. For this article, only data from the baseline period were analyzed to limit any confounding effects that the safety technology may have had on driving behavior. Results: Results indicated that when looking at age independent of sex, older drivers (age 60–70) took fewer trips, drove fewer minutes, were less likely to drive at night, and had fewer high decelerations and speeding events than the youngest age group (20–30). They were also less likely to drive during peak morning traffic and on high-speed roads than their middle-age counterparts (40–50). Across all age groups, there were few differences by sex, with the exception that females drove fewer miles and fewer minutes and had fewer high decelerations than males. When both age and sex were taken into account, it was often the group of females age 60–70 that appeared to account for many of the age and sex differences found in driving exposure and patterns. Conclusions: Future research in this area would benefit from larger scale and longitudinal study designs so that changes in driving exposure and patterns over time among large samples of drivers could be examined.

One of the challenges of conducting research on aging and driving is the measurement of driving behavior. Many studies rely on older drivers themselves to report how much, where, and when they drive. While these data are useful, research shows that drivers have difficulty accurately reporting not only their driving exposure, but also their driving patterns. The Longitudinal Research on Aging Drivers (LongROAD) study employed an innovative system to objectively measure all driving done by participants in their primary vehicle. As of May, 2016 there were 1,477 participants who had collectively taken more than 650,000 trips and driven more than 4,000,000 miles. On average, trip length was 6.3 miles, trip duration was 14.2 minutes, and 6.1 percent of trips were taken at night. About 65 percent of trips occurred within 15 miles of home. Further detail on the data collection system and driver behaviors will be provided in this presentation.

This paper provides an analysis of how communication performance between vehicles using Dedicated Short-range Communication (DSRC) devices varies by antenna mounting, vehicle relative positions and orientations, and between receiving devices. DSRC is a wireless technology developed especially for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. A frequency band near 5.9 GHz has been set aside in the US and other countries for exploring safety and other uses for road vehicles. DSRC devices installed onboard vehicles broadcast their location using global navigation space systems (GNSS), speed, heading, and other information. This can be used to study communication performance in many scenarios including: car-following situations, rear-end crash avoidance, oncoming traffic situations, left turn advisory, head-on crash avoidance and do-not-pass warnings. Message Capture Fraction and Packet Loss Duration highlight how these measures change with distance and orientation of the vehicles. Data used in this study address four years of real-world use with over 2500 vehicles, with antennas primarily in an aftermarket-style installation.

Connected vehicle wireless data communications can enable safety applications that may reduce crashes, injuries, and fatalities suffered on our roads and highways, as well as enabling reductions in traffic congestion and effects on the environment. As a critical part of achieving these goals, the U.S. Department of Transportation (USDOT) contracted with a Team led by Battelle to integrate and validate connected vehicle on-board equipment (OBE) and safety applications on selected Class 8 commercial vehicles and to support those vehicles in research and testing activities that provide information and data needed to assess their safety benefits and support regulatory decision processes. This final report summarizes all of the activities and accomplishments of this project. Hardware and software were developed to adapt safety applications to commercial vehicles. Stages of testing included benchtop, test track, driver acceptance clinics, and support for the Safety Pilot Model Deployment. Outreach consisted of a demonstration at a trade show, presentations at meetings, and other activities.