Design Criteria to Architect Continuous Experimentation for Self-Driving Vehicles
This work addresses the problem of managing complex software evolution for self-driving vehicles, but it is incremental as it builds on existing Continuous Experimentation methods from software-only products.
The paper tackles the challenge of enabling Continuous Experimentation for self-driving vehicles, which process massive data and require continuous software evolution, by proposing design criteria for software architecture and development processes, and describes their application in a self-driving vehicle laboratory.
The software powering today's vehicles surpasses mechatronics as the dominating engineering challenge due to its fast evolving and innovative nature. In addition, the software and system architecture for upcoming vehicles with automated driving functionality is already processing ~750MB/s - corresponding to over 180 simultaneous 4K-video streams from popular video-on-demand services. Hence, self-driving cars will run so much software to resemble "small data centers on wheels" rather than just transportation vehicles. Continuous Integration, Deployment, and Experimentation have been successfully adopted for software-only products as enabling methodology for feedback-based software development. For example, a popular search engine conducts ~250 experiments each day to improve the software based on its users' behavior. This work investigates design criteria for the software architecture and the corresponding software development and deployment process for complex cyber-physical systems, with the goal of enabling Continuous Experimentation as a way to achieve continuous software evolution. Our research involved reviewing related literature on the topic to extract relevant design requirements. The study is concluded by describing the software development and deployment process and software architecture adopted by our self-driving vehicle laboratory, both based on the extracted criteria.