Deep Neural Networks – only in combination with traditional computer vision: Page 2 of 7

November 15, 2018 //By Cyril Cordoba, Renesas
Deep Neural Networks – only in combination with traditional computer vision
It will still take some time before fully-autonomous vehicles come onto the streets - even though the automotive industry is currently discussing automated driving intensively. Developments are evolutionary. Step by step, the technologies must emerge that are powerful enough, require little energy and meet the ISO 26262 standards.

In the area of parking, the situation is similar. Initially, the driver is only assisted although the vehicle may also be able to park itself into a parking space. Renesas and Nissan recently announced their collaboration for ProPILOT Park, which is available in the latest Nissan Leaf. The Valet Parking at low speed is connected with level 4 and the driver does not need to control the car anymore. It is interesting to see how new technologies, which make a new feature possible, are being made accessible.

Highest expectations encounter low degree of maturity

The advancement of technologies and markets almost always follows a similar pattern. Initially, a new technology emerges as an innovation trigger that enables new applications. This is typically associated with high expectations on the customer side, which are in stark contrast to the maturity level of the technology. This is followed by a disillusionment phase, in which it will be decided whether a technology will be successful at all. After the disillusionment, a phase follows in which the technology not only shows what it can really do, but also achieves high growth rates. This is followed by the time when the technology is established (plateau of productivity) and shows low but stable growth rates (around 3 percent).


Fig. 2: Market adoption of new technologies

ADAS and AD technologies precisely follow this curve. Building on existing control technologies (e.g. steering control, drive control), GPS and HMI technologies, sensor technologies (radar, ultrasound, camera etc.) for Level 1 applications were developed years ago. These applications have already reached the plateau of productivity or are well on the way there. By 2015, the industry was concentrating on the development of technologies - for example, cameras and radar with more intelligence at lower cost - which now have the highest growth rates. Vehicles with Level 3 applications will be available starting 2019 for volume production, and although Level 4 and 5 are currently the most talked about, the maturity of the technologies is still quite low. This also applies to deep neural networks (DNNs), which are considered already to be very promising in the next years, but are expected to be even more relevant for Level 4 and 5 applications. But their efficacy still need to be proven across all cases.

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