I’ve always believed that relying solely on a vision-only perception system is not a viable path forward — even though Tesla currently remains the most advanced player in autonomous driving.
The so-called advantages of vision-only perception — lower cost and algorithmic maturity — are becoming less compelling in today’s highly competitive landscape. TOF and FMCW LiDAR technologies are rapidly dropping in price; even a relatively ordinary project I’ve worked on recently has adopted a solid-state TOF solution. Meanwhile, sensor fusion algorithms are inevitably maturing as well.
The depth maps generated by vision-only systems are essentially pseudo-3D representations. When the technology is immature, depth estimation tends to suffer from errors and is easily deceived by environmental conditions. In contrast, LiDAR can offer millimeter-level — and with FMCW, even sub-millimeter — precision, along with significantly improved resistance to interference.
Moreover, using neural networks to compute depth and perform object detection demands substantial computational power. LiDAR, on the other hand, directly outputs 3D data, greatly reducing the processing burden.
Vision-only perception systems are also highly dependent on lighting conditions and scene texture complexity. LiDAR remains stable under backlight, low-light, and textureless scenarios, and continues to function reliably even in adverse weather such as rain and fog.
The lack of safety redundancy in vision-only approaches makes them unreliable for use in such challenging conditions — and realistically, we can’t expect users to simply avoid driving at night or in bad weather.
The future of autonomous driving will most likely rely on multi-sensor fusion: a form of generalized perception that balances cost and robustness by drawing on the strengths of several modalities.
The goal should not be to vindicate vision-only perception as a principle, but to build heterogeneous systems in which vision provides the backbone and LiDAR supplies redundancy and precision where they matter most.
For high-level autonomous driving (L4 and above), TOF — and perhaps especially FMCW LiDAR — is therefore likely to become increasingly common.