Considerations about Continuous Experimentation for Resource-Constrained Platforms in Self-Driving Vehicles
This addresses the problem of enabling iterative software improvement in autonomous vehicles for developers and manufacturers, but it is incremental as it builds on existing CE practices from web-based systems.
The paper tackles the challenge of applying Continuous Experimentation (CE) to resource-constrained self-driving vehicles, outlining a concept to enable data-driven development despite strict safety and computational limitations.
Autonomous vehicles are slowly becoming reality thanks to the efforts of many academic and industrial organizations. Due to the complexity of the software powering these systems and the dynamicity of the development processes, an architectural solution capable of supporting long-term evolution and maintenance is required. Continuous Experimentation (CE) is an already increasingly adopted practice in software-intensive web-based software systems to steadily improve them over time. CE allows organizations to steer the development efforts by basing decisions on data collected about the system in its field of application. Despite the advantages of Continuous Experimentation, this practice is only rarely adopted in cyber-physical systems and in the automotive domain. Reasons for this include the strict safety constraints and the computational capabilities needed from the target systems. In this work, a concept for using Continuous Experimentation for resource-constrained platforms like a self-driving vehicle is outlined.