Deep Neural Networks – only in combination with traditional computer vision

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.

Renesas is one of the largest MCU/SoC manufacturer for the automotive market. This position is also to be maintained in the future, which is why the company is focusing on ADAS applications and autonomous driving (AD). These are two areas that promise high growth rates and for which Renesas - thanks to its core competences in the areas of control (MCUs) and computing (SoCs) - is particularly well positioned.

The SAE International’s J3016 standard identifies six levels (Level 0 to Level 5) of driving automation that range from “no automation” to “full automation”. Since 2010, driver assistance functions (ADAS) such as ACC (Automatic Cruise Control), LKA (Lane Keep Assist), LCA (Lane Change Assist) or CTA (Cross Traffic Alert) covering Level 1 have been available in vehicles. In the next step, these systems were integrated further to implement functions such as AEB (Automatic Emergency Braking) or TJA (Traffic Jam Assist). The highway pilot is a level-3 application, where the driver is still responsible and must be able to intervene at short notice.

Fig. 1: The evolution from driver assistance systems
to automated driving

We assume that commercial vehicles with the city pilot function will not be available in volume production before 2025 due to the increasing level of complexity. Level 5, in other words fully autonomous vehicles, is a degree of automation that will only be achieved in the long term.

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