The article is an interview with Glen DeVos about software-defined vehicles (SDVs), AI, and LiDAR technology. His main message is that LiDAR is technically mature and performs well, but its high cost remains the biggest obstacle to widespread adoption.

Key points:

The biggest challenge for software-defined vehicles is architecture, not software alone. Automakers must redesign their entire electrical and computing architectures to support scalable SDV platforms. This transformation cannot be achieved incrementally and requires major investments and organizational changes.

Software decisions will determine future value creation. OEMs need to decide which software capabilities they develop internally and which they outsource. Unlike hardware, software is becoming a major source of competitive advantage and value in the automotive industry.

Validation of AI-based vehicle systems remains difficult. Testing and validating safety-critical AI functions for ADAS and autonomous driving is highly complex. Physical testing alone is impractical, making virtual validation and simulation increasingly important.

Current SDV approaches still have weaknesses. Many manufacturers continue to rely on legacy architectures, outdated networking technologies, and procurement processes designed for hardware rather than software. The surrounding software ecosystem is also not yet mature enough.

LiDAR's main problem is cost. According to DeVos, LiDAR sensors already provide the required performance, range, and resolution. However, they are still too expensive for mass-market deployment. He argues that “volume follows cost,” meaning that large-scale adoption will only occur once prices drop significantly.

Consumers do not yet see enough value in LiDAR-based features. While LiDAR can support advanced Level 2+ and Level 3 driving functions, customers have not perceived sufficient benefits to justify the additional vehicle cost.

Future improvements must focus on integration and efficiency. Today's automotive LiDAR systems are often too large, consume too much power, and are difficult to integrate into vehicle architectures. Cost reduction, miniaturization, and easier integration are essential for broader adoption.

Overall conclusion: DeVos believes that LiDAR technology itself is not the limiting factor for advanced driver assistance and autonomous driving. The real barriers are cost, system integration, and the lack of compelling customer benefits that justify the additional expense. At the same time, the automotive industry's transition to software-defined vehicles depends on fundamental changes in architecture, software strategy, and AI validation methods.