Automated Driving System (ADS) generally consists of 3 functions 1) Recognition, 2) Planning, 3) Control. Precise vehicle localization and accurate recognition of objects (vehicle, pedestrian, lane, traffic sign, etc.) are typically based on high-definition dynamic maps and data from multiple sensors (e.g. Camera, LiDAR, Radar). Planners, especially those for optimal path and trajectory, tend to be computationally intensive. Many applications in ADS use machine learning techniques such as DNN (Deep Neural Network), which further increase the demand for computing power. To parallelly process massive tasks and data in real-time, scalable software and high-performance SoC (System on Chip) with many CPUs or processing cores, and hardware accelerators (e.g. GPU, DLA) have been adopted.

However, ADS software and SoC hardware architecture are so large and complex that software validation at later testing phase is inefficient and costly. Due to continuous ADS software evolution and iterations, software redesign will occur much more frequently than traditional automotive systems. The productivity of software validation must be improved to avoid the unacceptable bloat of required effort and time.

This paper explores how to obtain optimal ADS software scheduling design and how to enable agile ADS software V&V (Verification and Validation) in order to release the product in short development cycle. The proposed agile software V&V framework integrates the design verification with scheduling simulator in PC and the validation with debugging and tracing tools for the hardware target, which is usually an embedded board. We developed utility tools to make the proposed framework seamless and automated. The evaluation results indicate that the proposed framework can efficiently explore the optimal scheduling design (e.g. scheduling policy, thread priority, core affinity) satisfying several non-functional requirements (e.g. response time, CPU utilization) for ADS. We also proved that the framework is practical and can be incorporated into agile ADS software development by validating it through the project.

Key words: - Automated Driving System (ADS) - System on Chip (SOC) - Deep Neural Network (DNN) - Optimal Scheduling Design - Verification and Validation (V&V)

Dr. Yixiao Li, Nagoya University, JAPAN; Prof. Dr. Yutaka Matsubara, Nagoya University, JAPAN; Mr. Daniel Olbrys, Toyota Research Institute - Advanced Development, Inc., UNITED STATES; Mr. Kazuhiro Kajio, Toyota Research Institute - Advanced Development, Inc., JAPAN; Mr. Takashi Inada, Toyota Research Institute - Advanced Development, Inc., JAPAN; Prof. Dr. Hiroaki Takada, Nagoya University, JAPAN