Exploring the Potential Safety Impact of Automated Driving Systems Using Naturalistic Data

Abstract

Automated driving systems (ADS) have the possibility to remove humans from the primary driving task, which has the potential to eliminate all crashes due to human driver error. However, a large component preventing the wide-scale adoption of ADS is the ability to confidently determine when they are safe enough to deploy and achieve the associated societal acceptance. The question of "how safe is safe enough?" has consumed the industry for quite some time. Determining the threshold of "safe enough," however, has proven to be quite a complex problem. A common perspective suggests that as long as ADS are safer than human drivers, then they are safe enough to deploy. However, this perspective introduces several challenges, including determining an appropriate human driver baseline (some drivers are safer than others), defining the conditions and scale of comparison, and considering whether society is willing to accept current roadway risks, which result in over forty thousand fatalities each year [1]. In these assessments, it is relevant to note that most driving is routine. Thus, modest ADS deployments are rarely exposed to the complex scenarios that the 233 million licensed human drivers encounter across the United States [2]. In these rare situations, human drivers might outperform ADS unless we identify methods to characterize these events and better understand what we are expecting robotic drivers to achieve. Naturalistic driving data, which represents large-scale in situ data collections of everyday driving, are used within this dissertation to identify and validate events and scenarios that might challenge the capabilities of ADS, indicating areas where further development and validation may be required to achieve acceptable levels of safety. Specifically, this dissertation analyzes nearly three thousand safety-critical events (SCEs) that involve crashes and near-crashes in a variety of scenarios. Specific events are selected to evaluate conditions under which ADS may have difficulty navigating the situation correctly. The analysis focuses on surprise events, including scenarios in which line of sight (LOS) perception systems are obstructed and constrain system response, as well as events involving unpredictable behavior by other road users in which the subject driver is not at fault. This analysis suggests that ADS may not perform as expected in blind turns and hills, mixed-speed traffic, lane-change events with other vehicles around, scenarios with significant occlusion at high speeds, and in scenarios in which pedestrians are visually occluded. By investigating SCE scenarios that are believed to be challenging for ADS to navigate, this research shows that using a small set of naturalistic data has the potential to convey important information to wide-scale ADS deployment that simulation or closed-track testing based on contrived scenarios simply cannot achieve. Near-crash and crash-relevant events are especially crucial for fully understanding the complex driving task and should be studied further to completely assess ADS safety. Additionally, human drivers are generally good at performing evasive maneuvers that require a complex understanding of the surrounding environment. Such near-crash situations necessitate an intricate sequence of perception and response for ADS, which may not be fully understood based on data from the limited ADS deployments performed to date. This dissertation aims to inform selection of ADS testing scenarios, catalogue edge cases which challenge ADS and limit their ability to provide safe transport, and to identify the opportunity to improve ADS performance in such situations through inclusion Vehicle-to-Everything (V2X) communications which enable perception beyond LOS sensing.