Browsing by Author "Haus, Samantha Helen"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Effectiveness of Automatic Emergency Braking for Protection of Pedestrians and Bicyclists in the U.S.
    Haus, Samantha Helen (Virginia Tech, 2021-11-16)
    In the United States, there were 36,560 traffic-related fatalities in 2018, of which 20% were pedestrians, bicyclists, and other vulnerable road users (VRUs) [1]. Vulnerable road users are non-vehicle occupants who, because they are not enclosed in a vehicle, are at higher risk of injury in traffic crashes. While overall traffic fatalities in the US have been decreasing, pedestrian and bicyclist fatalities have been trending upward. Vehicle-based active safety features could avoid or mitigate crashes with VRUs, but are highly dependent on the ability to detect a VRU with enough time or distance. This work presents methods to examine the characteristics of vehicle-pedestrian and vehicle-bicycle crashes and near-crashes using a variety of data sources, assess the potential effectiveness of Automatic Emergency Braking (AEB) in avoiding and mitigating VRU crashes through modeling and simulation, and estimate the future benefits of AEB for VRU safety in the United States. Additionally, active safety features are most effective when behavior of VRUs can be anticipated, however, the behavior of pedestrians and bicyclists is notoriously unpredictable. Therefore, an approach to examine and categorize pedestrian behavior in response to near-crashes and crashes events is presented. Overall, findings suggest that AEB has great potential to avoid and mitigate collisions with pedestrians and bicyclists, but it cannot avoid all crashes even when an idealized AEB system is assumed. Most pedestrians and bicyclists were found to be visible for at least one second prior to the crash, but obstructions, the unpredictability of VRUs, and adverse weather/lighting conditions still pose challenges in avoiding and mitigating crashes with VRUs.
  • Thumbnail Image
    Modeling Driver Behavior and I-ADAS in Intersection Traversals
    Kleinschmidt, Katelyn Anne (Virginia Tech, 2023-12-20)
    Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25 to 93% of intersection crashes. The effectiveness of I-ADAS will be limited by driver's pre-crash behavior and other environmental factors. This study will characterize real-world intersection traversals to evaluate the effectiveness of I-ADAS while accounting for driver behavior in crash and near-crash scenarios. This study characterized real-world intersection traversals using naturalistic driving datasets: the Second Strategic Highway Research Program (SHRP-2) and the Virginia Traffic Cameras for Advanced Safety Technologies (VT-CAST) 2020. A step-by-step approach was taken to create an algorithm that can identify three different intersection traversal trajectories: straight crossing path (SCP); left turn across path opposite direction (LTAP/OD); and left turn across path lateral direction (LTAP/LD). About 140,000 intersection traversals were characterized and used to train a unique driver behavior model. The median average speed for all encounter types was about 7.2 m/s. The driver behavior model was a Markov Model with a multinomial regression that achieved an average 90.5% accuracy across the three crash modes. The model used over 124,000 total intersection encounters including 301 crash and near-crash scenarios. I-ADAS effectiveness was evaluated with realistic driver behavior in simulations of intersection traversal scenarios based on proposed US New Car Assessment Program I-ADAS test protocols. All near-crashes were avoided. The driver with I-ADAS overall helped avoid more crashes. For SCP and LTAP the collisions avoided increased as the field of view of the sensor increased in I-ADAS only simulations. There were 18% crash scenarios that were not avoided with I-ADAS with driver. Among near-crash scenarios, where NHTSA expects no I-ADAS activation, there were fewer I-ADAS activations (58.5%) due to driver input compared to the I-ADAS only simulations (0%).