Naturalistic rapid deceleration data: Drivers aged 75 years and older

Developing personalised braking and steering thresholds for driver support systems from SHRP2 NDS data

Examining the relationships between the factors associated with the crash development enabled the realisation of driver support systems aiming to proactively avert and control crash causation at various points within the crash sequence. Developing such systems requires new insights in personalised pre-crash driver behaviour with respect to braking and steering to develop crash prevention strategies. Therefore, the current study utilises Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS) data to investigate personalised steering and braking thresholds by examining the last stage of a crash sequence. More specifically, this paper carried out an in-depth examination of braking and steering manoeuvres observed in the final 30 s prior to safety critical events. Two algorithms were developed to extract braking and steering events by examining deceleration and yaw rate and another developed and applied to determine the sequence of the manoeuvres. Based on the analysis, thresholds for detecting emerging situations were recommended. The investigation of driver behaviour before the safety critical events, provides valuable insights into the transition from normal driving to safety critical scenarios. The results indicate that 20% of the drivers did not react to the impending event suggesting that they were not aware of the imminent safety critical situation. Future development of Advanced Driver Assistance Systems (ADAS) can focus on individual drivers' needs with tailored activation thresholds. The developed algorithms can facilitate driver behaviour and safety analysis for NDS while the thresholds recommended could be exploited for the design of new driver support systems.

Review of Studies on Older Drivers' Behavior and Stress-Methods, Results, and Outlook

This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers or considers participants according to various age groups. Nevertheless, the definition of ‘older driver’ varies not only across studies but also across different government reports. Although questionnaire surveys are widely used to affordably obtain massive data in a short time, they lack objectivity. In contrast, biomedical information can increase the reliability of a driving stress assessment when collected in environments such as driving simulators and on-road experiments. Various studies determined that driving behavior and stress remain stable regardless of age, whereas others reported degradation of driving abilities and increased driving stress among older drivers. Instead of age, many researchers recommended considering other influencing factors, such as gender, living area, and driving experience. To mitigate bias in findings, this literature review suggests a hybrid method by applying surveys and collecting on-road/in-vehicle data.

Medication use and driving patterns in older drivers: preliminary findings from the LongROAD study

Background

The potential for impaired driving due to medication use can occur at any age, though older adults are more likely to take multiple prescribed medications and experience side effects that may affect driving ability. The purpose of this study was to characterize the relationship between medications and driving safety behaviors.

Methods

Data for this study came from the five-site Longitudinal Research on Aging Drivers (LongROAD) project. Participants were active drivers, age 65–79 years at enrollment, and patients at one of the 5 participating sites. Medication names and doses were obtained at baseline based on the “brown-bag review” method. Medications were coded using the American Hospital Formulary Service system. Driving data were collected by a GPS accelerometer installed in the study participants’ main vehicles.

Results

Medication data were available for 2949 (98.6%) of the 2990 participants, and 2898 (96.9% of all participants) had both medication data and at least 30 recorded days of driving. The median number of medications taken per study participant was seven, with a range of 0–51. Total number of medications was significantly associated with a higher rapid deceleration rate. Certain medication classes were significantly associated with other driving outcomes, including central nervous system agents (more speeding events), hormones and gastrointestinal medications (more rapid decelerations), electrolytes (fewer rapid decelerations), and antihistamines (greater right to left turn ratio).

Conclusions

Older adult drivers are taking large quantities of prescription and non-prescription medications that may affect their driving safety. Certain medication classes are associated with potentially adverse driving patterns, such as speeding and rapid decelerations, while others are associated with potentially protective maneuvers, such as right hand turning. Further research is warranted to identify and mitigate potential adverse effects of such medications on driving safety in older adults.

Perils of using speed zone data to assess real-world compliance to speed limits

OBJECTIVE

Real-world driving studies, including those involving speeding alert devices and autonomous vehicles, can gauge an individual vehicle's speeding behavior by comparing measured speed with mapped speed zone data. However, there are complexities with developing and maintaining a database of mapped speed zones over a large geographic area that may lead to inaccuracies within the data set. When this approach is applied to large-scale real-world driving data or speeding alert device data to determine speeding behavior, these inaccuracies may result in invalid identification of speeding. We investigated speeding events based on service provider speed zone data.

METHODS

We compared service provider speed zone data (Speed Alert by Smart Car Technologies Pty Ltd., Ultimo, NSW, Australia) against a second set of speed zone data (Google Maps Application Programming Interface [API] mapped speed zones).

RESULTS

We found a systematic error in the zones where speed limits of 50-60 km/h, typical of local roads, were allocated to high-speed motorways, which produced false speed limits in the speed zone database. The result was detection of false-positive high-range speeding. Through comparison of the service provider speed zone data against a second set of speed zone data, we were able to identify and eliminate data most affected by this systematic error, thereby establishing a data set of speeding events with a high level of sensitivity (a true positive rate of 92% or 6,412/6,960).

CONCLUSIONS

Mapped speed zones can be a source of error in real-world driving when examining vehicle speed. We explored the types of inaccuracies found within speed zone data and recommend that a second set of speed zone data be utilized when investigating speeding behavior or developing mapped speed zone data to minimize inaccuracy in estimates of speeding.

Visual Sensory and Visual-Cognitive Function and Rate of Crash and Near-Crash Involvement Among Older Drivers Using Naturalistic Driving Data

Purpose

An innovative methodology using naturalistic driving data was used to examine the association between visual sensory and visual-cognitive function and rates of future crash or near-crash involvement among older drivers.

Methods

The Strategic Highway Research Program (SHRP2) Naturalistic Driving Study was used for this prospective analysis. The sample consisted of N = 659 drivers aged ≥70 years and study participation lasted 1 or 2 years for most participants. Distance and near visual acuity, contrast sensitivity, peripheral vision, visual processing speed, and visuospatial skills were assessed at baseline. Crash and near-crash involvement were based on video recordings and vehicle sensors. Poisson regression models were used to generate crude and adjusted rate ratios (RRs) and 95% confidence intervals, while accounting for person-miles of travel.

Results

After adjustment, severe impairment of the useful field of view (RR = 1.33) was associated with an increased rate of near-crash involvement. Crash, severe crash, and at-fault crash involvement were associated with impaired contrast sensitivity in the worse eye (RRs = 1.38, 1.54, and 1.44, respectively) and far peripheral field loss in both eyes (RRs = 1.74, 2.32, and 1.73, respectively).

Conclusions

Naturalistic driving data suggest that contrast sensitivity in the worse eye and far peripheral field loss in both eyes elevate the rates of crash involvement, and impaired visual processing speed elevates rates of near-crash involvement among older drivers. Naturalistic driving data may ultimately be critical for understanding the relationship between vision and driving safety.