Evaluation of Difficulty for Autonomous Vehicles Testing Roads based on Multiple Criteria Decision Analysis Feng Chia Univ

Format:

Book

Conference/Event

Author/Creator:

Lin, Da-Jie, author.

Contributor:

CHANG, CHUN-YI

Chen, Ming Hsu

CHOU, AI-CHEN

Huang, Pin-Cheng

Liu, Xinxian

WU, CHENG HSIN

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Autonomous Vehicles are being widely tested under diverse conditions with expectations that they will soon be a regular feature on roads. The development of Autonomous Vehicles has become an important policy in countries around the world, and the technologies developed by countries and car manufacturers are different, and at the same time to adapt to the road environment and traffic management facilities of different countries, so some countries have built self-driving test sites, and the test content is also different, so it is impossible to compare its relative difficulty. This study surveyed experts and scholars to develop a means of weighting the respective difficulty of various autonomous vehicle testing conditions based on the analytic hierarchy process and fuzzy analytic hierarchy process, applied to a sample of 33 sets of testing conditions based on road type, management actions and operational capabilities. Weights are also adjusted in response to environmental impact factors to determine the relative difficulty of different scenarios for each test route. Research results show that "multiple intersections", "on-site traffic control" and " emergency stop " exert the highest weights. Increased overall route complexity or uncertainty results creates greater operational challenges for autonomous vehicles. The results provide a useful reference for future road testing systems and route classifications for autonomous vehicles. In addition, this study conducts a trial calculation of the difficulty of an autonomous vehicle test route. Total difficulty is calculated using six indicators, including the route's total score, number of scenarios, along with the mean, standard deviations and maximum and minimum scores for each scenario. The results of this study can be used to evaluate the difficulty of test scenarios in existing test sites or as a guideline for future test site planning and design

Notes:

Vendor supplied data

Publisher Number:

2024-01-1983

Access Restriction:

Restricted for use by site license