Comparative Analysis of Critical Lane Change for Autonomous Driving Vehicles and Human Drivers

Authors

H. Li MSc, F. De Cristofaro  MSc, F. Orucevic BSc, Z. Gu BSc, Assoc. Prof. Dipl.-Ing. Dr. techn. A. Eichberger, Institute of Automotive Engineering, Graz University of Technology:

Year

2023

Print Info

Production/Publication ÖVK

Summary

In today's industry, the need for trustworthy and safe high-driving automation algorithms for Autonomous Driving (AD) vehicles is rapidly growing. This growth is being driven by the expansion of the market and the enforcement of new safety regulations all around the world.

The mainstream approach today is to test autonomous vehicles in critical scenarios modelled by real-life traffic data or natural trajectory databases to understand if and how an AD algorithm is improving road safety. In this paper, we compare the behaviours of human drivers and AD vehicles when dealing with critical scenarios of lane change manoeuvres on highways. The tested AD algorithm is based on the Apollo Open Autonomous Driving Platform (AOADP) integrated into the simulation. Additionally, we extract a critical human driving scenario from the HighD dataset (a dataset of naturalistic vehicle trajectories recorded on German highways) and use this scenario to test the AD algorithm. Eventually, the behaviours of the AD vehicles and human drivers are compared and analyzed. The simulation results reveal that the parameter configuration of the path planning algorithm significantly impacts the execution of lane changes. The comparison between human driving behaviour and AD systems is one of the key factors in specifying the sensing technology. The proposed approach opens a new door for quantitative analysis of the impact on autonomous driving measurements, thus contributing to a safe AD function specification.

ISBN

978-3-9504969-2-5