Characterizing driver speeding behavior when using partial-automation in real-world driving.

Objective: Speeding is a prevalent and complex risky behavior that can be affected by many factors. Understanding how drivers speed is important for developing countermeasures, especially as new automation features emerge. The current study seeks to identify and describe types of real-world speeding behaviors with and without the use of partial-automation.Methods: This study used a combination of supervised and unsupervised data analysis techniques to assess relevant factors in real-world speeding epochs, extracted from the MIT Advanced Vehicle Technology Naturalistic Driving Study, and classified them into distinct speeding behaviors. Speeding epochs were defined as traveling at least 5 mph over the speed limit for a minimum duration of 3 s. Vehicle speed-exceedance profiles were characterized over time using Dynamic Time Warping and included in multivariate models that evaluated the associations between different features of the speeding epochs, such as speeding duration and magnitude. Finally, the identified features were used to cluster speeding behaviors using the Gower dissimilarity measure.Results: The analysis yielded four types of behaviors in both partially-automated and manual driving: (i) Incidental speeding (low duration, low magnitude), (ii) Moderate speeding (low duration, moderate magnitude), (iii) Elevated speeding (moderate duration, high magnitude), and (iv) Extended speeding (long duration, high magnitude). When comparing the behaviors with and without partial-automation use, both Incidental and Moderate speeding were found to have significantly longer durations with partial-automation than manual driving. Elevated speeding was found to be more prevalent and associated with higher magnitudes during manual than with partially-automated driving. Finally, although Extended speeding was more prevalent during automation use, it was associated with a lower mean and maximum speed magnitude compared to Extended speeding during manual driving.Conclusions: This work highlights the variability in speeding behavior between and within partially-automated and manual driving. The design of systems that mitigate risky speeding behaviors should consider targeting divergent behaviors observed between manual and automated driving as a mechanism to mitigate the prevalence of the different behaviors associated with each state.

Differential benefit of sensor system field-of-view and range in pedestrian automated emergency braking systems.

OBJECTIVE: Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a combination of radar and cameras to detect pedestrians and automatically apply braking to prevent or mitigate an impending collision. However, these current sensor systems might have a restricted field-of-view (FOV) which may not detect all pedestrians. Advanced sensors like LiDAR can have a wider FOV that may substantially help improve detection. The objective of this study was to determine the influence of FOV and range on the effectiveness of P-AEB systems to determine the potential benefit of advanced sensors. METHODS: This study utilized vehicle-pedestrian crashes from the Pedestrian Crash Data Study (PCDS) to calculate pre-crash pedestrian and vehicle trajectories. A computational model was then applied to simulate the crash with a hypothetical P-AEB system. The model was designed to be able to vary the system's field-of-view (FOV), range, time-to-collision of activation, and system latency. In this study we estimated how the FOV and range of advanced sensors could affect P-AEB system effectiveness at avoiding crashes and reducing impact speed. Sensor range was varied from 25 - 100 m and sensor FOV was varied from ±10° to ±90°. RESULTS: Sensors simulated with a range of 50 m or greater performed only approximately 1% better than with a 25 m range. Field-of-view had a larger effect on estimated system avoidance capabilities with a ± 10° FOV sensor estimated to avoid 46-47% of collisions compared to 91-92% for a ± 90° FOV sensor. The system was able to avoid a greater percentage of cases in which the vehicle was traveling straight at sensor FOVs of ±30° and below. Among the unavoided crashes with a sensor FOV of ±90°, the average impact velocity using a 100 m range sensor was 7.4 m/s which was 3.1 m/s lower than a 25 m range sensor. CONCLUSIONS: Sensor ranges above 25 m were not found to significantly affect estimated crash avoidance potential, but had a small effect on impact mitigation. Sensor FOV had a larger effect on crash avoidance up to a FOV of ±60° with little additional benefit at larger FOVs.

Estimated benefit of automated emergency braking systems for vehicle-pedestrian crashes in the United States.

Objective: The objective of this research study is to estimate the benefit to pedestrians if all U.S. cars, light trucks, and vans were equipped with an automated braking system that had pedestrian detection capabilities. Methods: A theoretical automatic emergency braking (AEB) model was applied to real-world vehicle-pedestrian collisions from the Pedestrian Crash Data Study (PCDS). A series of potential AEB systems were modeled across the spectrum of expected system designs. Both road surface conditions and pedestrian visibility were accounted for in the model. The impact speeds of a vehicle without AEB were compared to the estimated impact speeds of vehicles with a modeled pedestrian detecting AEB system. These impacts speeds were used in conjunction with an injury and fatality model to determine risk of Maximum Abbreviated Injury Scale of 3 or higher (MAIS 3+) injury and fatality. Results: AEB systems with pedestrian detection capability, across the spectrum of expected design parameters, reduced fatality risk when compared to human drivers. The most beneficial system (time-to-collision [TTC] = 1.5 s, latency = 0 s) decreased fatality risk in the target population between 84 and 87% and injury risk (MAIS score 3+) between 83 and 87%. Conclusions: Though not all crashes could be avoided, AEB significantly mitigated risk to pedestrians. The longer the TTC of braking and the shorter the latency value, the higher benefits showed by the AEB system. All AEB models used in this study were estimated to reduce fatalities and injuries and were more effective when combined with driver braking.