Analysis of autopilot disengagements occurring during autonomous vehicle testing

Abstract

In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle's safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers (SAE) Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.

Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing

“…Studies in [8], [10]- [12], [36] provide descriptive statistics on AV safety and reliability. Both [9] and [4] conclude that road testing alone is inadequate evidence of AV safety, and argue the need for alternative methods to supplement public road testing.…”

“…These studies mostly rely on descriptive statistics, giving insights on various aspects of AV safety [8], [10]- [12]. A RAND Corporation study [9] has been highly cited, and in this paper we refer to it for comparison, to illustrate similarities and differences between alternative statistical approaches to assessment and the results thereof.…”

Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

“…Most of the existing research lies in the scopes of driver behaviours, driver attention and intention, driver drowsiness and fatigue, driver cognitive and neural muscles, etc. All of these studies have a common objective, which is to gain a better understanding of driver status from either a psychological or physiological aspect so as to assist in driving tasks and increase driving safety [12][13][14].…”

A bi‐level model to optimize road networks for a mixture of manual and automated driving: An evolutionary local search algorithm

“…On the other hand, analyses of autopilot disengagement reports occurring during AV tests in the United States show that road surface conditions (including poor markings) is one of the main causes of disengagements (Lv et al., ). According to Dixit, Chand, and Nair (), 9.98% of disengagements have happened due to road infrastructure (which is the second cause after system failure), 5% due to other road users, and 56.1% due to system failures which can also be associated with vehicle's disability to deal with the environment.…”