Machine Learning Integration in ADAS

Embedded vision solutions are one among the critical parameter of an autonomous vehicle. It provides vision to an automotive along with the arrays of sensors that aid in interpreting the content from captured images and respond consequently. Numerous safety measures have been integrated and enhanced in recent years such as shatter-resistant glass, 3-point seatbelts, and airbag; these measures were designed to mitigate losses in the occurrence of any untoward incident.

The advancements such as ‘Advanced Driver Assistance Systems’ (ADAS) would assist autonomous vehicles to pave its way into the commercial world. Various features such as blind spot detection, lane departure, Keep-aid will assist the driver to take immediate measure to avoid the collision. Such technical innovation has boosted demand for active safety measure in automobiles along with existing passive safety components such as automatic seat belt, airbags, and others. These driver assistance technologies help drivers in better driving experience by improving the driving procedure, monitoring the driver decisions and avoiding prompt mishaps. Such advancement has definitely raised the extent of safety measures, although, driverless vehicles are still a distant dream, which requires instant decision-making capabilities, learning driver patterns in traffics, sharp maneuvers and so on.

Advanced Driver Assistance Systems
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With the snowballing computational capacity, semiconductors had shown the great possibility to make it into a reality. In image classification, convolutional neural network algorithm exhibited a significant promise in terms of accuracy over the conventional vision algorithms. With the enhanced accuracy, image classification techniques, detection, and recognition have been gaining significant traction in developed economies such as the U.S, U.K, Japan, and others. The evolution of deep neural networks allows the system to be learned from the previous incidences and train itself automatically. Although, the challenge is to learn incidences with mathematical equations and evaluate complex driving patterns such as unsafe cut-in or unsafely pulling into traffic. Thus, the entire learning relied on mathematically driven driving patterns, traffic maneuvers and imminent responses of such occurrences. This is the main concern within the industry and overcoming such huddle would certainly aid the adoption of Automotive Vehicle

’Autonomous vehicles will be seen as the future automotive industry, thus hardware and software will play a prominent role and likely to be a bit more complex and advanced that allows the system to take a decision within to nanoseconds else it would have a grave and severe impact in terms of loss of property and life. Thus, emphasis on testing would be more strict and stringent of such vehicles to ensure proper and flawless operation on the road, which in turn help to safeguard individuals and properties. Integration of ADAS applications such as Active Cruise Control, Lane Departure Warning Systems, Autonomous Emergency Braking System (AEBS), Forward Collision Mitigation System (FCMS) with Machine Learning will aid system to take imminent decision with the help of sensors in case of presence of an object on the road. The safety systems suppliers and automobile manufacturers designed the architecture of the driverless vehicle by combining driver assistance technologies. ADAS is witnessing remarkable growth across the globe and embedding of machine learning algorithms with ADAS would likely to change the dynamics of the industry completely.

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