After the hype of autonomous driving, the automotive industry has understood that higher-level autonomy increasingly requires the involvement of AI-based solutions. Nevertheless, current brute-force AI solutions need a tremendous amount of data and are computationally expensive, which leads to the long-tail problem in self-driving and thus the questionable reliability and profitability of AV business. On the other hand, automotive mass-production needs to enhance driver safety and its own sustainability, and to have new products with new technology to attract end users. Younger generations are obsessed with high-tech and want to see more increased autonomy in their vehicles, so many Tier 1s and OEMs are paying great attention to autonomous parking, smart summon, auto-reverse and highway pilot. With the recent technological advancements in automotive SoC, computer vision and robotics, realizing these advanced functions at a great scale is feasible, and thus L2+ is considered a very good intermediate step to leverage the autonomy level.

• Why higher levels of vehicle autonomy require deeper AI integration—and the challenges posed by current data-hungry, brute-force AI methods.
• How combining AI-driven and deterministic approaches can help deliver advanced driver-assistance functions while maintaining safety and reliability.
• The importance of developing next-generation AI that can address reasoning tasks and improve explainability for more trustworthy autonomous systems.
• When and where affordable autonomous vehicle (AV) mass production is expected to emerge—particularly in regions with mature supporting infrastructure.