Autonomous driving systems (ADSs) have the potential to enhance safety through advanced perception and reaction capabilities, reduce emissions by alleviating congestion, and contribute to various improvements in quality of life. Despite significant advancements in ADSs, several real-world accidents resulting in fatalities have occurred due to failures in the autonomous driving perception modules. As a critical component of autonomous vehicles, LiDAR-based perception systems are marked by high complexity and low interpretability, necessitating the development of effective testing methods for these systems. Current testing methods largely depend on manual data collection and labeling, which restricts their ability to detect a diverse range of erroneous behaviors. This process is not only time-consuming and labor-intensive, but it may also result in the recurrent discovery of similar erroneous behaviors during testing, hindering a comprehensive assessment of the systems.In this paper, we propose and implement a fuzzing framework for LiDAR-based autonomous driving perception systems, named LDFuzz, grounded in metamorphic testing theory. This framework offers the first uniform solution for the automated generation of tests with oracle information. To enhance testing efficiency and increase the number of tests that identify erroneous behaviors, we incorporate spatial and semantic coverage based on the characteristics of point cloud data to guide the generation process. We evaluate the performance of LDFuzz through experiments conducted on four LiDAR-based autonomous driving perception systems designed for the 3D object detection task. The experimental results demonstrate that the tests produced by LDFuzz can effectively detect an average of 7.5% more erroneous behaviors within LiDAR-based perception systems than the optimal baseline. Furthermore, the findings indicate that LDFuzz significantly enhances the diversity of failed tests.

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), our approach promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation to iteratively produce offspring, balancing the evolutionary efficiency and diversity. During the evolutionary process, to navigate away from the local optima, our approach integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM's quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate our approach in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that our approach can significantly improve the efficiency and diversity of the evolutionary search.

Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored.To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle’s perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large-scale study and identify the following key findings: (1) The LiDAR-based cooperation configuration exhibits the highest perception performance; (2) Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.

Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for more comprehensive and targeted testing approaches to ensure safe driving. Scenarios from real-world accident reports provide valuable resources for ADS testing, including critical scenarios and high-quality seeds. However, existing scenario reconstruction methods from accident reports often exhibit limited accuracy in information extraction. Moreover, due to the diversity and complexity of road environments, matching current accident information with the simulation map data for reconstruction poses significant challenges.

Environment configuration is the basis for software reuse, enabling developers to reuse specific functions.However, the lack of uniform practice in dependency declaration specifications of Python projects can cause problems for developers trying to install third-party libraries.Existing package management tools are often inadequate to help fix these problems.Fixing these errors requires expensive hours and domain knowledge for developers.To help address related problems, some studies focus on well-maintained and popular Python projects about dependency conflict problems caused by PIP's installation rules.However, many projects in the wild are outside of this scope.We carefully investigate 110 issues in 110 projects in the wild.Based on the comprehensive study, we design and implement EasyPip to automatically detect and fix problems in Python dependency declaration files.Dif-

Autonomous Driving Systems (ADSs) are safety-critical, and must be fully tested before being deployed on real-world roads. To comprehensively evaluate the performance of ADSs, it is essential to generate various safety-critical scenarios. Most of existing studies assess ADSs either by searching high-dimensional input space, or using simple and pre-defined test scenarios, which are not efficient or not adequate. To better test ADSs, this paper proposes to automatically generate safety-critical test scenarios for ADSs by influential behavior patterns, which are mined from real traffic trajectories. Based on influential behavior patterns, a novel scenario generation technique, CRISCO, is presented to generate safety-critical scenarios for ADSs testing. CRISCO assigns participants to perform influential behaviors to challenge the ADS. It generates different test scenarios by solving trajectory constraints, and improves the challenge of those non-critical scenarios by adding participants’ behavior from influential behavior patterns incrementally. We demonstrate CRISCO on an industrial-grade ADS platform, Baidu Apollo. The experiment results show that our approach can effectively and efficiently generate critical scenarios to crash ADS, and it exposes 13 distinct types of safety violations in 12 hours. It also outperforms two state-of-art ADS testing techniques by exposing more 5 distinct types of safety violations on the same roads.

Autonomous Driving Systems (ADSs) are safety-critical systems, and safety violations of Autonomous Vehicles (AVs) in real traffic will cause huge losses. Therefore, ADSs must be fully tested before deployed on real world roads. Simulation testing is essential to find safety violations of ADS. This paper proposes MOSAT, a multi-objective search-based testing framework, which constructs diverse and adversarial driving environment to expose safety violations of ADSs. Specifically, based on atomic driving maneuvers, MOSAT introduces motif pattern, which describes a sequence of maneuvers that can challenge ADS effectively. MOSAT constructs test scenarios by atomic maneuvers and motif patterns, and uses multi-objective genetic algorithm to search for adversarial and diverse test scenarios. Moreover, in order to test the performance of ADS comprehensively during long-mile driving, we design a novel continuous simulation testing technique, which runs the scenarios generated by multiple parallel search processes alternately in the simulator and can continuously create different perturbations to ADS. We demonstrate MOSAT on an industrial-grade platform, Baidu Apollo, and the experimental results show that MOSAT can effectively generate safety-critical scenarios to crash ADSs and it exposes 11 distinct types of safety violations in a short period of time. It also outperforms state-of-the-art techniques by finding more 6 distinct safety violations on the same road.