Autonomous driving road network generation and evaluation based on generative artificial intelligence

Shanding Ye; Tao Li; Guoqing Yang; Pan Lv; Hong Li; Zhijie Pan

doi:10.1117/12.3024107

14 February 2024 Autonomous driving road network generation and evaluation based on generative artificial intelligence

Shanding Ye, Tao Li, Guoqing Yang, Pan Lv, Hong Li, Zhijie Pan

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130183I (2024) https://doi.org/10.1117/12.3024107
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

The increasing number of vehicles has led to issues such as traffic congestion, air pollution, and energy consumption. Intelligent connected autonomous driving (AD) offers a solution to these transportation challenges, but it requires cooperation among dedicated AD road networks, connected cloud-based control and dispatch systems, and intelligent vehicle sensing. To harness the potential of AD, it is crucial to consider the requirements of autonomous vehicles (AVs) when planning road networks. Well-designed road networks can enhance travel experiences, reduce energy consumption, and promote urban sustainability. In order to meet the needs of AD while ensuring the operational efficiency of urban transportation networks, this paper introduces a road network evaluation framework that encompasses both static and dynamic assessment criteria, following a detailed exploration and analysis of various road network generation methods. This evaluation framework addresses the deficiency in existing road network generation methods, which lack dynamic assessment of traffic networks. To validate the proposed approach, we have chosen a road network generation method based on conditional generative adversarial network (CGAN) as an application example. Through carefully designed experiments, we confirm the feasibility and effectiveness of the proposed method for generating and evaluating road networks tailored for AD scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shanding Ye, Tao Li, Guoqing Yang, Pan Lv, Hong Li, and Zhijie Pan "Autonomous driving road network generation and evaluation based on generative artificial intelligence", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130183I (14 February 2024); https://doi.org/10.1117/12.3024107

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Roads

Artificial intelligence

Network architectures

Transportation

Design

Autonomous driving

Simulations

Show All Keywords

Keywords/Phrases

Search In:

Publication Years