While our focus here is not centrally on the algorithms developed for self-driving cars, the types of algorithms and paradigms used will have an impact on our ability to benchmark and evaluate them. This facet of algorithms is often forsaken for performance.
- Are some algorithm types more challenging to evaluate?
- What additional challenges are presented by data-driven approaches?
- Are the paradigms that we have currently (model-free, model-based, reinforcement, imitation, etc.) sufficient or do we need fundamentally different approaches?
