Think of this as a special research focus on making the AI co‑pilot in modern cars safer and harder to hack. It’s about the brains behind lane-keeping, automatic braking, and self-driving features—how to ensure they don’t make dangerous mistakes and can’t be easily manipulated.
OEMs and Tier‑1s need rigorous, shareable methods to make AI-based driver assistance and autonomy safe, robust, and cyber-secure. This focus issue aggregates research and standards-oriented thinking to reduce accidents from AI failures, close security gaps in connected vehicles, and provide frameworks that regulators and insurers can rely on.
SAE’s role as a standards and publications body gives it access to OEMs, Tier‑1 suppliers, academics, and regulators. The moat comes from convening power, alignment with formal safety and security standards, and the resulting influence on how the industry defines ‘acceptable’ AI-driven ADAS safety practices.
Hybrid
Unknown
High (Custom Models/Infra)
Real-time inference under strict latency and power constraints, combined with data privacy and secure over-the-air update requirements for safety-critical software.
Early Majority
This is not a commercial product but a focused technical venue shaping how AI-based ADAS safety and security should be engineered. Its differentiation is the concentration on safety and cybersecurity for AI in autonomous and connected vehicles, rather than generic ADAS or generic AI safety; it is tightly coupled to the automotive standards ecosystem and regulatory expectations.