Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data.

Objectives: With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment.

Comparison of Waymo rider-only crash data to human benchmarks at 7.1 million miles.

Objectives: This article examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely.

Methods: ADS crash data were derived from NHTSA's Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in Phoenix, Arizona, San Francisco, California, and Los Angeles, California, and compared to human benchmarks from the literature.

Comparative safety performance of autonomous- and human drivers: A real-world case study of the Waymo Driver.

After several years of public road testing, the commercial deployment of fully autonomous vehicles-or Automated Driving Systems (ADS)-is poised to scale substantially following significant technological advancements and recent regulatory approvals. However, the fundamental question of whether an ADS is safer than its human counterparts remain largely unsolved due to several challenges in establishing an appropriate real-world safety comparison method. As scaling ensues, the lack of an established method can contribute to misinterpretations or uncertainties regarding ADS safety and impede the continuous and consistent assessment of ADS performance.

Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain.

Preventing and mitigating high severity collisions is one of the main opportunities for Automated Driving Systems (ADS) to improve road safety. This study evaluated the Waymo Driver's performance within real-world fatal collision scenarios that occurred in a specific operational design domain (ODD). To address the rare nature of high-severity collisions, this paper describes the addition of novel techniques to established safety impact assessment methodologies.

Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes.

When faced with an imminent collision threat, human vehicle drivers respond with braking in a manner which is stereotypical, yet modulated in complex ways by many factors, including the specific traffic situation and past driver eye movements. A computational model capturing these phenomena would have high applied value, for example in virtual vehicle safety testing methods, but existing models are either simplistic or not sufficiently validated. This paper extends an existing quantitative driver model for initiation and modulation of pre-crash brake response, to handle off-road glance behavior.

Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity.

Studies show high correlations between drivers' off-road glance duration or pattern and the frequency of crashes. Understanding drivers' use of peripheral vision to detect and react to threats is essential to modelling driver behavior and, eventually, preventing crashes caused by visual distraction. A between-group experiment with 83 participants was conducted in a high-fidelity driving simulator.

Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel.

Objective: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation.

Background: Securing driver engagement-by mitigating irony of automation (i.e.

Investigating the correspondence between driver head position and glance location.

The relationship between a driver's glance orientation and corresponding head rotation is highly complex due to its nonlinear dependence on the individual, task, and driving context. This paper presents expanded analytic detail and findings from an effort that explored the ability of head pose to serve as an estimator for driver gaze by connecting head rotation data with manually coded gaze region data using both a statistical analysis approach and a predictive (i.e.

Definition of run-off-road crash clusters-For safety benefit estimation and driver assistance development.

Single-vehicle run-off-road crashes are a major traffic safety concern, as they are associated with a high proportion of fatal outcomes. In addressing run-off-road crashes, the development and evaluation of advanced driver assistance systems requires test scenarios that are representative of the variability found in real-world crashes. We apply hierarchical agglomerative cluster analysis to define similarities in a set of crash data variables, these clusters can then be used as the basis in test scenario development.

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis.

Objective: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework.

Background: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood.

Method: Based on existing models of driver attention, an explanatory framework was outlined.

Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses.

Adaptive Cruise Control (ACC) has been shown to reduce the exposure to critical situations by maintaining a safe speed and headway. It has also been shown that drivers adapt their visual behavior in response to the driving task demand with ACC, anticipating an impending lead vehicle conflict by directing their eyes to the forward path before a situation becomes critical. The purpose of this paper is to identify the causes related to this anticipatory mechanism, by investigating drivers' visual behavior while driving with ACC when a potential critical situation is encountered, identified as a forward collision warning (FCW) onset (including false positive warnings).

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies.

Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario's kinematics, quantified in terms of visual looming of the lead vehicle on the driver's retina.

Eye movement and brake reactions to real world brake-capacity forward collision warnings--a naturalistic driving study.

The purpose of this field operational test study is to assess visual attention allocation and brake reactions in response to a brake-capacity forward collision warning (B-FCW), which is designed similarly to all forward collision warnings on the market for trucks. Truck drivers' reactions immediately after the warning (threat-period) as well as a few seconds after the warning (post-threat-recovery-period) are analyzed, both with and without taking into consideration the predictability of an event and driver distraction. A B-FCW system interface should immediately direct visual attention toward the threat and allow the driver to make a quick decision about whether or not to brake.