The software used to implement advanced functionalities in critical domains (e.g. autonomous operation) impairs software timing. This is not only due to the complexity of the underlying high-performance hardware deployed to provide the required levels of computing performance, but also due to the complexity, non-deterministic nature, and huge input space of the artificial intelligence (AI) algorithms used. In this paper, we focus on Apollo, an industrial-quality Autonomous Driving (AD) software framework: we statistically characterize its observed execution time variability and reason on the sources behind it. We discuss the main challenges and limitations in finding a satisfactory software timing analysis solution for Apollo and also show the main traits for the acceptability of statistical timing analysis techniques as a feasible path. While providing a consolidated solution for the software timing analysis of Apollo is a huge effort far beyond the scope of a single research paper, our work aims to set the basis for future and more elaborated techniques for the timing analysis of AD software.
The advanced AI-based software used for autonomous driving comprises multiple highly-coupled modules that are data and control dependent. Deploying those alreadyintegrated software frameworks makes unit testing, a fundamental step in the validation process of critical software, very challenging in safety-critical systems. To tackle this issue, in this paper, we show the steps we followed to develop standalone versions of the modules in an industry-level autonomous driving framework (Apollo) by applying several modifications to its architectural design. We show how the standalone modules have the same functional behavior as their integrated counterpart modules. We exemplify the benefits of standalone modules by performing incremental analysis of the software timing requirements of each module running on a heterogeneous System on Chip (SoC). This is a mandatory step to consolidate and integrate software modules guaranteeing timing constraints (e.g. related to freedom from interference) while maximizing SoC utilization.Index Terms-Unit testing, autonomous driving, Apollo1 The residual risk is the amount of risk remaining after specific risk mitigation measures are put in place.
Software resource usage testing, including execution time bounds and memory, is a mandatory validation step during the integration of safety-related real-time systems. However, the inherent complexity of Autonomous Driving (AD) systems challenges current practice for resource usage testing. This paper exposes the difficulties to perform resource usage testing for AD frameworks by analyzing a complex and critical module of an AD framework, and provides some guidelines and practical evidence on how resource usage testing can be effectively performed, thus enabling end users to validate their safety-related real-time AD frameworks.
Driven by the improvements in a variety of domains, autonomous driving is becoming a reality and today, industry aims at moving toward fully autonomous vehicles. High-tech chip manufacturers are designing highperformance and energy-efficient platforms in accordance with safety standard requirements. However, the software used to implement advanced functionalities in autonomous vehicles challenges real-time constraints on those platforms. Hence, there is a clear need for industry-level autonomous driving benchmarks to evaluate platforms and systems. In this paper, we propose ADBench, a benchmarking approach and benchmark suite for state-of-the-art autonomous driving platforms, in accordance with the key modules, structural design and functions of AD systems, building on several industry-level autonomous driving systems. The use of standard benchmarks facilitates the design, verification and validation process of autonomous systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.