Model-based Development for Autonomous Driving Software Considering Parallelization
By: Kenshin Obi , Takumi Onozawa , Hiroshi Fujimoto and more
In recent years, autonomous vehicles have attracted attention as one of the solutions to various social problems. However, autonomous driving software requires real-time performance as it considers a variety of functions and complex environments. Therefore, this paper proposes a parallelization method for autonomous driving software using the Model-Based Development (MBD) process. The proposed method extends the existing Model-Based Parallelizer (MBP) method to facilitate the implementation of complex processing. As a result, execution time was reduced. The evaluation results demonstrate that the proposed method is suitable for the development of autonomous driving software, particularly in achieving real-time performance.
Similar Papers
A Systematic Digital Engineering Approach to Verification & Validation of Autonomous Ground Vehicles in Off-Road Environments
Robotics
Tests self-driving trucks on rough roads safely.
Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems
Software Engineering
Finds car software bugs before they cause crashes.
A Modular Architecture Design for Autonomous Driving Racing in Controlled Environments
Robotics
Drives cars safely on test tracks by themselves.