Profile Dynamic Memory Allocation in Autonomous Driving Software

The software-defined vehicle has driven the autonomy and electrification of the automotive industry. A technical challenge for software designers is how to leverage existing software from AI research and autonomous driving (AD) development and make it useful, reliable, and efficient for customer requirements and functional safety standards. However, the software is critical in autonomous driving (AD) systems, where it should ensure reliability and real-time guarantee simultaneously. Further, the AD industry may re-utilize existing mature software implementation (e.g., C++ STL libraries) in order to accelerate development. However, the jeopardy of reliability and real-time guarantee caused by dynamic memory management inside remains a major concern for practitioners in the field. This paper presents a software tool (called MemTrace) to conveniently analyze the dynamic memory management behavior of AD software and provide important analytical results for software designers to make judgments on software quality, run-time efficiency, and safety with high confidence. MemTrace relies on interception and instrumentation for profiling the explicit allocation behavior of general software, as well as the implicit memory allocations of using C++ STL containers and smart pointers. The profiling data will be analyzed for the behavior that could jeopardize software safety, for example, memory leak and memory external fragmentation. Our experiment results show that MemTrace can effectively provide detailed periteration results for AD software modules, and identify potential memory-related hazards. Through the profiling of prototype AD software with MemTrace, we have gained 6 insightful observations, including the potential risks associated with prolonged usage, and suggestions for effective utilization of STL containers and smart pointers in AD software, which can assist AD software developers during the development process.