Abstract
Following a very brief look at the human vision system, an extended summary of our own elemental steps towards future vision systems for ground vehicles is given, leading to the proposal made in the main part. The question is raised of why the predominant solution in biological vision systems, namely pairs of eyes (very often multi-focal and gaze-controllable), has not been found in technical systems up to now, though it may be a useful or even optimal solution for vehicles too. Two potential candidates with perception capabilities closer to the human sense of vision are discussed in some detail: one with all cameras mounted in a fixed way onto the body of the vehicle, and one with a multi-focal gaze-controllable set of cameras. Such compact systems are considered advantageous for many types of vehicles if a human level of performance in dynamic real-time vision and detailed scene understanding is the goal. Increasingly general realizations of these types of vision systems may take all of the 21st century to be developed. The big challenge for such systems with the capability of learning while seeing will be more on the software side than on the hardware side required for sensing and computing.
Highlights
The acceptance of technical vision systems by the human driver depends much on the differences in performance the overall system shows in comparison to his own capabilities
All these groups coming from Computer Science (CS) and Artificial Intelligence (AI) started by investigating rather complex vision tasks at a much slowed-down rate on available computing hardware, while at the same time massively parallel computer systems were investigated for analyzing sequences of digital images [11]
The partially separated processing of image data both in the eye and in the visual cortex of the human vision system based on carbon molecules is not mandatory for technical vision systems based on silicon molecules
Summary
The acceptance of technical vision systems by the human driver depends much on the differences in performance the overall system shows in comparison to his own capabilities. An outlook on potential future paths of development in automotive vision systems is given in Sections 4 and 5. 20th century has seen a wide spread of designs for ground vehicles—the 21st century will see the development of a wide variety of technical vision systems for vehicles, including deep neural nets. These developments are not a matter of years but of decades
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