State of the Art: Ongoing Research in Assessment Methods for Lane Keeping Assistance Systems

Abstract

A safe, comfortable and intelligent Lane Keeping Assistance System (LKAS) is fundamental on the way to realizing full automated driving. In spite of the current high market penetration of LKAS, vehicle manufacturers, suppliers and researchers are still working on improving the system to reach a higher customer acceptance. To achieve this goal more efficiently, a systematic and ideally objective assessment procedure of the system needs to be established. Till date, a thorough review study of the assessment procedures of LKAS does not exist, especially of those which also take the subjective impression of the driver into consideration. The motivation of this paper is to benchmark the state-of-the-art assessment methods of LKAS and identify where problems and research potentials lie. The paper summarizes the relevant characteristics of LKAS based on representative research in the past decades. It also compares the characterization of LKAS with the field of steering feel and vehicle handling in the aspects of procedure, strategy, as well as their advantages and disadvantages. This paper contributes to transferring the know-hows and valuable experiences from other well-developed fields of vehicle dynamics into the rapid-evolving branch of Advanced Driver Assistance System (ADAS), in order to help standardizing its assessment procedure and give insights on system design from a human-centered perspective in the early development phase, thus shortening the ADAS development cycles.

Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers

“…The sensors collect data from various parts of the vehicle and forward the information to different systems. Sensors are part of different functions as [29]- [31]:…”