With the increased adoption of advanced driver assistance systems (ADAS), the automotive industry continues to embrace greater driver automation. As the advancement of ADAS will require more 'intelligent' decision making, it has become a good fit for developments in neural networks and deep learning. These technologies are now used in ADAS to deliver the early stages of full autonomy; Level 1, 2 and soon 3. However, there is still a long way to go before we reach full autonomy and ADAS is still an evolving area of research for automotive manufacturers. The term ADAS existed long before AI was introduced to the vehicle, and it will ultimately become replaced by Autonomous Driving when AI is fully in control. Between now and then, we can expect many exciting developments in the area of semi-autonomous features.
As the main objective of ADAS is to increase driver comfort and safety, embedding machine learning into ADAS makes a lot of sense. These systems will not suffer from fatigue, or slow reactions; they will be designed to operate at the best of their abilities at all times. Just as ADAS today is used to relieve the driver of certain actions or provide greater visibility of the road and its users, when enabled by machine learning these systems will initially work alongside the driver. As we grow more accepting of these systems, we will become more dependent on them. This change in the driver dynamic will not happen quickly, nor will we see a sudden change from fully manual to completely autonomous vehicles.
According to Global Market Insights, the automotive sector is using AI in various ways. For example, deep learning is being used to train neural networks so they can react and behave like human drivers. Vision systems will be a big application area for AI, this can already be seen in the way ADAS are now able to identify road signs. Natural language processing is another area where AI is a good fit, with the precedent already set here in smart speakers in our homes. Machine learning in general will be a sector of its own. This will likely cover various systems around the vehicle that are currently monitored using sensors and controlled through ECUs. The introduction of machine learning into these systems will support inferencing that will, in turn, lead to more efficient vehicles, lower cost of maintenance and longer service lives.
Hardware platforms for more intelligent ADAS development
It is well reported that the automotive industry is extremely cost-sensitive, so any new technology must be introduced with this consideration. For larger OEMs targeting a high-end customer base, ADAS is more common and typically implemented, at an architectural level, in a centralized way. This puts the majority of the processing in a single ECU, which has many advantages, such as optimized system performance and reduced design complexity. However, the processing requirements in a centralized architecture can be high, using multicore processors with a large silicon area. An alternative approach, adopted by many manufacturers for mid-range models, is to use a distributed architecture based on more modest processors.
For manufacturers looking to develop a solution based on a centralized architecture NXP has introduced the BlueBox series of development platforms. While this is intended to help developers working on Level 4/5 self-driving vehicles, it includes the S32V234 automotive vision and sensor fusion processor, which can also be used in a distributed architecture to develop semi-autonomous features (Level 1 and 2).
The S32V234 is based on a quad Arm Cortex-A53 64-bit processor cluster and integrates an image signal processor (ISP), a 3D graphics processor (GPU) and dual APEX-2 vision accelerators. As it is intended for use in ADAS and autonomous vehicles, it is designed and manufactured to deliver automotive-grade reliability, with the functional safety and security required. As well as being suitable for various ADAS applications, such as object detection and recognition using surround view systems, it is also the perfect platform for developing automotive systems that use machine learning and neural networks.
Software tools for automotive AI system development
As with most things, AI has a hierarchy. In terms of the various technologies involved, at the very deepest level are neural networks. These are the algorithms that implement deep learning. In turn deep learning can be considered a subset of machine learning, which refers to systems that can make decisions based on inferencing. Above this sits AI, which is a more general term to describe all of the above technologies.