Managing driving modes in automated driving systems

TL;DR

This paper presents a comprehensive framework for managing driving modes in semi-autonomous vehicles, integrating decision analysis, Bayesian forecasting, and statistical modeling to improve safety and driver intervention management.

Contribution

It introduces novel algorithms and a statistical modeling framework for driving mode management and early warning systems in semi-autonomous vehicles.

Findings

Algorithms effectively manage driving modes in simulations

Bayesian forecasting improves intervention timing

Framework supports safety in mixed autonomy scenarios

Abstract

Current technologies are unable to produce massively deployable, fully autonomous vehicles that do not require human intervention. Such technological limitations are projected to persist for decades. Therefore, roadway scenarios requiring a driver to regain control of a vehicle, and vice versa, will remain critical to the safe operation of semi-autonomous vehicles for the foreseeable future. Herein, we adopt a comprehensive perspective on this problem taking into account the operational design domain, driver and environment monitoring, trajectory planning, and driver intervention performance assessment. Leveraging decision analysis and Bayesian forecasting, both the support of driving mode management decisions and the issuing of early warnings to the driver are addressed. A statistical modeling framework is created and a suite of algorithms are developed to manage driving modes and issue relevant warnings in accordance with the management by exception principle. The efficacy of these developed methods are then illustrated and examined via a simulated case study.