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Accelerating the Journey to Autonomous for Carmakers

Heterogeneous hardware, open, flexible software are keys to improved safety and efficiency for automotive industry

2 min read
Autonomous car factory

A child runs into the street, stands frozen and terrified as she sees a car speeding toward her. What's safer? Relying on a car with autonomous capabilities or a human driver to brake in time?

In that situation, it takes the average driver 1.5 seconds to react and hit the brakes; it takes a car equipped with vision systems, RADAR and LIDAR just 0.5 seconds. That 3x faster response time can mean the difference between life and death, and it's one of many reasons the automotive industry is accelerating down its journey to autonomous. Whether it's safety, efficiency, comfort, navigation or the more efficient manufacture of an autonomous car, automotive companies are increasingly embracing autonomous technologies.

But this wave of interest in autonomous technologies does not come without its challenges. Disparate systems and a dizzying range of software and hardware choices can make charting a path to autonomy challenging. For example, many current solutions for automated driving are based on bulky, high-power, costly standalone chips. Others require proprietary solutions, which severely constrain design flexibility. However, to make it suitable for widespread commercial use, Tier1 and OEMs are looking for a more power-efficient and cost-effective solution suitable for mass production.

To embrace the vision, we need to understand that autonomous workloads are fundamentally different from traditional workloads and confront the challenges that complexity presents. The incorporation of AI and machine learning means the workload magnitude is far greater when compared to something like the narrow scope of operation surrounding, for example, contemporary ADAS (automated driver assistance systems).

Grappling with complexity

Autonomous is also more complex than traditional workloads because of the overlapping and interconnected nature of systems in industrial applications. There may be a machine vision system with its own set of data and algorithms that needs to integrate with robot vehicles that are alerted by the vision system that a task has been completed and pickup is required.

Integration may also be required for other points along the supply chain. For example, vehicles rolling down the manufacturing line will benefit from just-in-time parts arrivals thanks to the more integrated, near real-time connection with the supply chain.

These technologies also need to support features to help achieve both ASIL D and ASIL B safety requirements, as well as ISO26262.

imgJourney to autonomous automotivePhoto: Arm

New thinking, new technology

All of this requires a fundamental reconsideration of design, validation and configuration as well as embracing emerging technologies and methodologies.

As we evolve toward increasingly autonomous deployment, we need new hardware and software systems that will replace some of the commercial offerings being used today – and a fundamental shift from homogeneity to heterogeneous systems. These new systems must not only deliver flexibility, but help reduce the power, size, cost and thermal properties while retaining the performance needed to run autonomous workloads. And such systems will benefit from a flourishing ecosystem coalescing around these new design approaches that offer customers choice and flexibility to unleash their innovations.

Our mission at Arm is to deliver the underlying technologies and nurture an ecosystem to enable industries such as automotive to accelerate their journey to autonomy and reap the rewards of delivering new, transformative products to their customers.

In this market deep dive we outline the technology challenges and hardware and software solutions that can propel the automotive industry forward in its journey to a more safe, efficient and prosperous future.

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Europe Expands Virtual Borders To Thwart Migrants

Our investigation reveals that Europe is turning to remote sensing to detect seafaring migrants so African countries can pull them back

14 min read
A photo of a number of people sitting in a inflatable boat on the water with a patrol ship in the background.

Migrants in a dinghy accompanied by a Frontex vessel at the village of Skala Sikaminias, on the Greek island of Lesbos, after crossing the Aegean sea from Turkey, on 28 February 2020.

ASSOCIATED PRESS

It was after midnight in the Maltese search-and-rescue zone of the Mediterranean when a rubber boat originating from Libya carrying dozens of migrants encountered a hulking cargo ship from Madeira and a European military aircraft. The ship’s captain stopped the engines, and the aircraft flashed its lights at the rubber boat. But neither the ship nor the aircraft came to the rescue. Instead, Maltese authorities told the ship’s captain to wait for vessels from Malta to pick up the migrants. By the time those boats arrived, three migrants had drowned trying to swim to the idle ship.

The private, Malta-based vessels picked up the survivors, steamed about 237 kilometers south, and handed over the migrants to authorities in Libya, which was and is in the midst of a civil war, rather than return to Malta, 160 km away. Five more migrants died on the southward journey. By delivering the migrants there, the masters of the Maltese vessels, and perhaps the European rescue authorities involved, may have violated the international law of the sea, which requires ship masters to return people they rescue to a safe port. Instead, migrants returned to Libya over the last decade have reported enslavement, physical abuse, extortion, and murders while they try to cross the Mediterranean.

If it were legal to deliver rescued migrants to Libya, it would be as cheap as sending rescue boats a few extra kilometers south instead of east. But over the last few years, Europe’s maritime military patrols have conducted fewer and fewer sea rescue operations, while adding crewed and uncrewed aerial patrols and investing in remote-sensing technology to create expanded virtual borders to stop migrants before they get near a physical border.

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