BA-TRACE: Boundary-Aware Trace Reconstruction for Scenario-Based Evaluation of Mixed AUTOSAR Adaptive and ROS 2 Vehicular Embedded Systems
Shunsuke Ito, Ryudai Iwakami, Hiroyuki Hanyu, Tasuku Ishigooka, Takuya Azumi
Abstract
Modern vehicular embedded systems increasingly combine ROS 2-based autonomous-driving stacks with AUTOSAR Adaptive Platform (AUTOSAR AP). Such mixed stacks make scenario-based evaluation hard to interpret because execution paths cross DDS-SOME/IP middleware boundaries between ROS 2 and AUTOSAR AP. Existing simulators and tracing tools execute scenarios or collect platform-local traces but cannot reconstruct cross-domain data flows. This paper presents BA-TRACE, a boundary-aware trace reconstruction framework for scenario-based evaluation of mixed AUTOSAR AP and ROS 2 vehicular embedded systems. BA-TRACE combines ROS 2 trace events, AUTOSAR ara::log events, ARXML-derived structural dependencies, and bridge-level instrumentation to reconstruct an end-to-end execution graph across the DDS-SOME/IP boundary. A case study with an AWSIM/OpenSCENARIO-based object-detection and braking scenario shows that BA-TRACE reconstructs the expected cross-platform path and exposes boundary-specific latency such as point-cloud transfer overhead. The reconstructed topology is used as evidence of traceability, not as proof of behavioral correctness or safety.
Create a lesson
Related papers
Spotlights: Discovering Improvement Opportunities in Software Repositories
Udi Barzelay, Ophir Azulai, Idan Friedman et al.
Assessing the Construct Validity of Object-Oriented, Class-Level Code Quality Metrics
Hera Arif, Miikka Kuutila, Paul Ralph
AdaRepair-Mem: Adaptive Experience Orchestration for Repository-Level Program Repair
Z. C. Luo, J. C. Guo, W. J. He et al.
HapCiD: Detecting API-related Compatibility Issues in OpenHarmony Apps
Daihang Chen, Yonghui Liu, Mingyi Zhou et al.
EviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause Analysis
Yuhao Wang, Zhen Qin, Xingliang Wang et al.
A Closed-Loop Control Architecture for Reliable Constraint Satisfaction in LLM Text Generation
Quan Zhou, Shahbaz Siddeeq, Mika Saari et al.