Route coverage testing for autonomous vehicles via map modeling

• Main Authors: TANG, Yun, ZHOU, Yuan, WU, Fenghua, LIU, Yang, SUN, Jun, HUANG, Wuling, WANG, Gang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Intelligent Transportation Systems; Autonomous Vehicle Navigation; Performance Evaluation and Benchmarking; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6219
• Institution: Singapore Management University

Autonomous vehicles (AVs) play an important role in transforming our transportation systems and relieving traffic congestion. To guarantee their safety, AVs must be sufficiently tested before they are deployed to public roads. Existing testing often focuses on AVs' collision avoidance on a given route. There is little work on the systematic testing for AVs' route planning and tracking on a map. In this paper, we propose CROUTE, a novel testing method based on a new AV testing criterion called route coverage. First, the map is modeled as a labeled Petri net, where roads, junctions, and traffic signs are modeled as places, transitions, and labels, respectively. Second, based on the Petri net, we define junctions' topology features and route features for junction classification. The topology feature describes the topology of roads forming the junction, and the route feature identifies the actions that a vehicle can take to follow a route. They can characterize route types on a map. Hence, route coverage measures how many route types are covered. We then propose a systematic method that aims to cover all route types for a well-designed AV system with a small number of test cases. We implement and evaluate CROUTE on Baidu Apollo running with the LGSVL simulator. We carry out testing on the map from a section of San Francisco and find six different types of issues in Apollo. The experiment results show the validity of route coverage and the efficiency of CROUTE