Collision Avoidance Training Using a Driving Simulator in Drivers with Parkinson\u27s Disease: A Pilot Study

Parkinson’s disease (PD) impairs driving performance, and simulator studies have shown increased crashes compared to controls. In this pilot study, eight drivers with PD participated in three drive sessions with multiple simulator intersections of varying visibility and traffic load, where an incurring vehicle posed a crash risk. Over the course of the three sessions (once every 1-2 weeks), we observed reduction in crashes (p=0.059) and reaction times (p=0.006) to the vehicle incursion. These findings suggest that our simulator training program is feasible and potentially useful in drivers with PD. Future research questions include transfer of training to different driving tasks, duration of benefit, and the effect on long term real life outcomes in comparison to a standard intervention (e.g., driver education class) in a randomized trial

Differences in Simulated Car Following Behavior of Younger and Older Drivers

Older drivers are at risk for vehicle crashes due to impairments of visual processing and attention, placing these drivers at greater risk in driving tasks that require continuous attention to neighboring traffic, especially lead vehicles (LVs). We investigated car following behavior in 42 younger drivers (ages 18 to 44 years) and 58 older drivers (ages 65 to 86 years) in a driving simulator. The drivers were instructed to maintain two car lengths from a virtual LV. The LV varied its velocity according to a sum of three sine waves, making the velocity changes unpredictable to the drivers. A Fourier analysis was performed using the vehicle trajectory data to derive measures of coherence, gain, and delay as indices of car following behavior. These measures as well as headway distance were compared between the two groups. Older drivers were less able to match changes in the LV velocity indicated by lower coherence (0.76 v. 0.84, p=0.019) and larger gain (2.24 v. 1.74, p=0.031). However, these drivers followed further behind the LV than younger drivers, a potential compensatory strategy that may reduce collision risk for older drivers

The Relationship Between Driving Behavior and Entropy

OBJECTIVES High variability in the lateral control of a vehicle may result in an increased likelihood of accidents. Boer (2000) proposed a method of quantifying variability in steering wheel position, termed “entropy” (scaled between 0 and 1). In this approach, the steering wheel position at each time point is predicted based on the position at the three preceding time points, and the discrepancies between the predicted and observed values are utilized to define a baseline distribution of prediction errors within a subject. This distribution is then used as a reference for calculating a summary “entropy” metric in follow-up segments of driving, such as when a driver may be distracted when using a cell phone. This same concept has also been applied to the lateral position of a vehicle (Dawson et al., 2006). The objective of this study was to ascertain whether entropy was affected by behavioral factors such as steering techniques and speed. We hoped to gain insight regarding the usefulness of entropy measures, and the appropriate interpretation of statistical tests based on entropy. METHODS We designed a simple driving route in a simulator known as SIREN (Rizzo, 2004), with a straight road segment of 3.7 km, followed by an S-curved road segment of 3.8 km. Using an expert driver familiar with the simulator, we performed a factorial experiment with different steering techniques (normal driving, swerving, and steering using a rigid grip and sudden “jerks”) and driving speeds (35 mph, 55 mph, and 75 mph). Data on steering wheel position and lane position were collected at 30 frames per second, and then reduced to 5-frame blocks of 167 msec each. Based on these blocks, we estimated the baseline parameter to characterize the prediction errors for each drive during the straight road section, and then applied this parameter to the straight and curved road sections in order to calculate entropy. This approach was used for both steering and lane position entropy. The data were analyzed using multiple linear regresssion to assess the affects of steering technique and speed, adjusting for road curvature. We also calculated Pearson correlation coefficients to measure the association between steering and lane position entropy. RESULTS Data were obtained on a total of 40 drive segments. Steering entropy ranged from 0.34 to 0.90, with a mean (SD) of 0.56 (0.16). Lane position entropy ranged from 0.34 to 0.93, with a mean (SD) of 0.61 (0.15). Compared to normal driving, steering behavior involving jerking motions tended to lower the steering entropy by 0.14 (p=0.012), and tended to lower the lane position entropy by 0.25 (p<0.001). Swerving in wide lateral motions had no effect on steering entropy, and only a minor effect on lane position entropy, decreasing it by 0.07. Compared to driving at 35 mph, driving at either 55 mph or 75 mph increased the steering entropy by an average of 0.08, but had no effect on lane position entropy. Although not our primary focus, we found that driving in curved sections tended to have higher entropy measures (increase of 0.21 for steering and 0.20 for lane position; p<0.001 in both cases). Despite a few outliers, the correlation between steering and lane position entropy was found to be high (r=0.84; see Figure 1). CONCLUSIONS Although entropy is often considered as an increasing function of workload, and would presumably increase in non-optimal conditions, some unsafe driving behaviors are actually negatively associated with entropy. Safe driving often involves making frequent minor steering adjustments, especially in curved sections of the road, which might lead to an increase in the entropy measure. If a driver rigidly holds onto the steering wheel and then makes large corrections when approaching or crossing a lane boundary, the fixed steering wheel position over several seconds may actually cause an apparent decrease in entropy. In summary, entropy may be a useful tool in quantifying vehicular control, but caution should be exercised when interpreting the results, as the associations involving entropy are not always in the anticipated direction

Ascertainment of On-Road Safety Errors Based on Video Review

Using an instrumented vehicle, we have studied several aspects of the on-road performance of healthy and diseased elderly drivers. One goal from such studies is to ascertain the type and frequency of driving safety errors. Because the judgment of such errors is somewhat subjective, we applied a taxonomy system of 15 general safety error categories and 76 specific safety error types. We also employed and trained professional driving instructors to review the video data of the on-road drives. In this report, we illustrate our rating system on a group of 111 drivers, ages 65 to 89. These drivers made errors in 13 of the 15 error categories, comprising 42 of the 76 error types. A mean (SD) of 35.8 (12.8) safety errors per drive were noted, with 2.1 (1.7) of them being judged as serious. Our methodology may be useful in applications such as intervention studies, and in longitudinal studies of changes in driving abilities in patients with declining cognitive ability

Impaired Curve Negotiation in Drivers with Parkinson’s Disease

OBJECTIVE: To assess the ability to negotiate curves in drivers with Parkinson’s disease (PD). METHODS: Licensed active drivers with mild-moderate PD (n= 76; 65 male, 11 female) and elderly controls (n= 51; 26 male, 25 female) drove on a simulated 2-lane rural highway in a high-fidelity simulator scenario in which the drivers had to negotiate 6 curves during a 37-mile drive. The participants underwent motor, cognitive, and visual testing before the simulator drive. RESULTS: Compared to controls, the drivers with PD had less vehicle control and driving safety, both on curves and straight baseline segments, as measured by significantly higher standard deviation of lateral position (SDLP) and lane violation counts. The PD group also scored lower on tests of motor, cognitive, and visual abilities. In the PD group, lower scores on tests of motion perception, visuospatial ability, executive function, postural instability, and general cognition, as well as a lower level of independence in daily activities predicted low vehicle control on curves. CONCLUSION: Drivers with PD had less vehicle control and driving safety on curves compared to controls, which was associated primarily with impairments in visual perception and cognition, rather than motor functio