Need For Machine Learning Based Simulator Approach To ADAS Validation

The global ADAS market is growing at a rapid pace and is expected to reach $ 60 bn by 2020 at a CAGR of 23 %, according to a report by Allied Market Research [1] with Europe and North America being the key geographies driving this growth. In order to make way for this rapid growth, investments are being made by various constituents of the value chain such as silicon vendors, OEMs, and sensor manufacturers along others to capture a pie of this market. This is resulting in innovations in chip design, artificial intelligence (AI) and deep learning, radar, and computer vision-based solutions, which are key to realising vehicle autonomy.

As ADAS features become more mainstream, one of the major challenges industry faces is achieving economies of scale so that the technology moves to mid-tier segments, where the volumes are present. Another challenge is the lack of standardisation for exchange of information between various sensors, vision based systems, radars, et cetera, in a car’s network.

Increasing system complexity due to multiple sub-systems being integrated in autonomous cars and the need to process the output data in real-time is also becoming a challenge. As standalone ADAS features transition to autonomy, cars will be making decisions based on AI and deep learning, and would hence require lengthy and expensive validation cycles. With regulations for autonomous cars still evolving, it becomes necessary for technology providers to ensure that the systems are compliant with increasing regulatory requirements. The issue of liabilities in case of mishaps is a complex question that does not have a simple answer. This therefore requires a validation strategy that is aligned with this changing and evolving landscape.

As per a study conducted by Rand Corporation [2], autonomous vehicles have to be driven for 275 mn miles to demonstrate with 95 % confidence that their failure rate is almost 1.09 fatalities per 100 mn miles. This is a huge ask and gives a perspective of the validation task that lies ahead for the industry. This is primarily due to the fundamental departure from state flow-based design, where co-relation between input and output can be established to machine learning systems, and where the decision taken is not so apparent since it is based on probabilistic models and also on what the machine has learnt over a period of time.