scenoRITA: Generating Less-Redundant, Safety-Critical and Motion Sickness-Inducing Scenarios for Autonomous Vehicles

Abstract

There is tremendous global enthusiasm for research, development, and deployment of autonomous vehicles (AVs), e.g., selfdriving taxis and trucks from Waymo and Baidu. The current practice for testing AVs uses virtual tests-where AVs are tested in software simulations-since they offer a more efficient and safer alternative compared to field operational tests. Specifically, searchbased approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, systematically creating valid and effective tests for AV software remains a major challenge. To address this challenge, we introduce scenoRITA, a test generation approach for AVs that uses evolutionary algorithms with (1) a novel gene representation that allows obstacles to be fully mutable, hence, resulting in more reported violations, (2) 5 test oracles to determine both safety and motion sickness-inducing violations, and (3) a novel technique to identify and eliminate duplicate tests. Our extensive evaluation shows that scenoRITA can produce effective driving scenarios that expose an ego car to safety critical situations. scenoRITA generated tests that resulted in a total of 1,026 unique violations, increasing the number of reported violations by 23.47% and 24.21% compared to random test generation and state-of-the-art partially-mutable test generation, respectively.

In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data

“…In Autoware software, point cloud library is used to filter and interpret point clouds to manage LiDAR scans and 3D mapping data, as well as visualization functions; compute unified device architecture is for handling the computation-intensive tasks involved in self-driving; convolutional architecture for fast feature embedding is a deep learning framework and OpenCV for image processing (Xin et al, 2022). Apollo is another open-source, Unity-based platform that is widely used in simulating autonomous vehicles created by Baidu (Almanee et al, 2021). Apollo encompasses a set of perception, routing, planning, localization, prediction, control, CanBus, HD-Map, human machine interface (HMI), monitor, and guardian modules, while it supports three glog, gflags, and ROS libraries (Alcon et al, 2020).…”