Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Abstract

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.

Computational load reduction of the agent guidance problem using Mixed Integer Programming

“…Trajectory planning methods, on the other hand, search for routes that respect the movement constraints along time. For example, while in [18] a linear regression model predicts the evolution of human mobility in regions of a megalopolis, [19] uses statistical modeling to investigate the hierarchical structure of accident causes in autonomous vehicles and [20] proposes a proportional-integral-derivative (PID) controller for the real-time robotic stabilization of a robotic arm to act upon a dynamically moving human with a tumor.…”

“…In state-space, the continuous-time model is _ x ¼ A c x þ B c u, with state vector x T = [r x v x r y v y ] and control vector u T = [a x a y ]. Then, matrices A c and B c are given by Eqs(18) and(19).…”

Introductory Chapter: Analysis and Prevention of Accidents

“…Murat Darçın* and Caner Filiz Gendarmerie and Coast Guard Academy, Turkey *Address all correspondence to: mrtdrc@yahoo.com DMV (Department of Motor Vehicles) of California, shares the data of traffic accidents and according to related data, there are 234 accidents in which AVs are involved that happened in California from January 1, 2014 to December 31, 2019. However, this number does not mean that in all cases, the AVs are responsible [18]. In most cases, AVs are not the primary actors in these accidents, because they are equipped with several systems to prevent accidents such as lasers, high-powered cameras, radars, and even sonars.…”

Turing in the driver's seat: Can people distinguish between automated and manually driven vehicles?

“…The concern here is that automated and manual driving might be quite different in terms of setting appropriate expectations about their future behavior for drivers of manually drive vehicles following them. Reports on collisions with self‐driving vehicles suggest that the majority are being rear‐ended by drivers of manual vehicles (Wang & Li, 2019). Being able to differentiate between a vehicle being driven by an automated algorithm and a manually driven vehicle essentially represents the on‐road equivalent of the “Turing test.” In 1950, Alan Turing (Turing, 1950) argued that to pass his test, a machine would need to exhibit intelligent behavior equivalent to, or at least indistinguishable from, that of a human.…”

“…What is not yet known is how drivers of other, manually driven, vehicles will react to the presence of automated vehicles on the road in front of them. There is some suggestion that automated vehicles do not necessarily behave like manually driven vehicles and that drivers following such vehicles might not anticipate their behavior correctly (Wang & Li, 2019). This is important, as their perception of a car driven by an autopilot, rather than one driven by a human, may change the way in which they interact with it on the road (Thompson, Read, Wijnands, & Salmon, 2020).…”