Home > Storage > PowerScale (Isilon) > Industry Solutions and Verticals > Analytics > Dell Technologies Solution: Distributed Deep Learning Infrastructure for Autonomous Driving > Overview
Computer vision and machine learning (ML) solutions are integrated into automobiles to improve safety, convenience, and the driver experience. Sensor data coming from multiple sources such as camera, lidar, radar and ultrasonic sensors are processed and used to extract information and characteristics. Modern vehicles have adopted active safety systems – such as anti-lock braking and autonomous emergency braking (AEB), as they move from passive safety systems such as seat-belt pre-tensioning and airbags (designed to minimize injury during an accident).
With the evolution of artificial intelligence (AI) and deep learning (DL), it is possible to develop embedded control units (ECUs) into vehicles with computer vision algorithm that can interpret content from images and cloud point data and make corresponding predictions or actions.
The significant automotive safety improvement in the past was passive safety, designed to minimize damage during an accident. Advanced driver assistance systems (ADAS) can proactively help the driver to avoid accidents by using innovative deep learning (DL) technologies. For example, blind spot detection can alert a driver as they try to move into an occupied lane. Pedestrian detection notifies the driver that pedestrians are in front of or behind the car. AEB activates the brakes to avoid an accident or pedestrian injury. More ADAS features combine with sensor fusion. These bring us closers to the goal of an autonomous vehicle. As the level of accuracy and sophistication increases, autonomous driving (AD) can realize full capabilities. The critical success is the combination of improved safety algorithms, increased computational power, and access to large, comprehensive verification datasets.
This paper is exploring the infrastructure challenges faced by automotive Original Equipment Manufacturers (OEMs), Tier-1s, and Tier-2s in developing DL algorithms for ADAS / AD. Meanwhile, this paper also proposes a scale-out compute and storage solution. Optimized for ADAS / AD workloads - delivering high performance, high concurrency, massive scalability, and flexibility. It is optimized for ADAS / AD workloads - delivering high performance, high concurrency, massive scalability, and flexibility