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Processing advances: living on the edge of next-level AVs - Automotive World

To realise autonomous driving, the industry needs deep learning models efficient enough to run on existing hardware, writes Yonatan Geifman

Advanced driver assistance systems (ADAS) have progressed considerably in recent years. Blind spot detection, lane assistance, and collision avoidance are just a few of the systems closing the technological gap on the road towards true vehicular autonomy. But to borrow a quote from our impatient backseat passengers, “Are we there yet?” Not quite. There are still roadblocks between where the industry stands and where it’s trying to go. But each obstacle the automotive industry encounters is a catalyst for innovation, bringing us closer to an era of mobility in which autonomous driving is not just ubiquitous, but incredibly safe.

The issue often boils down to the software models that power ADAS applications. These deep learning models execute in-vehicle data processing in real-time and require huge amounts of computer power to run. As a result, when building in-vehicle applications, developers with limited computational resources find themselves too often confined to choosing between speed or accuracy. However, with safety hanging in the balance, the stakes are simply too high to compromise on either.

Unless speed and accuracy can both be sufficiently prioritized, advances in autonomous applications will remain in second gear. One way to overcome this obstacle? Well, car lovers know all about tuning; it’s all about getting the right mix of variables—speed, reliability—to achieve peak engine performance. The same principle can be applied toward optimizing deep learning models.

Driving into the deep end

The deep learning models embedded into ADAS systems process huge amounts of data collected from cameras and other sensors in the vehicle. With all that data, deep learning models empower autonomous vehicles (AVs) to “think”, or rather, process information almost instantaneously, in order to make the split-second decisions human drivers make on the road every day.

Each obstacle the automotive industry encounters is a catalyst for innovation

Picture a vehicle cruising down a street when suddenly a ball bounces onto the road. The vehicle has to process this input, predict the possible trajectory of a child running after it, and choose the safest course of action—brake, swerve, etc. Since the cost of error is so high, ADAS systems need to be incredibly fast and accurate. Neither can come at the expense of the other. But developers are struggling to deliver on both equally, despite advances in deep learning models and edge devices.

While some experts have suggested cloud processing as an alternative, this solution comes with its own risks. Connecting to distant third-party cloud-based resources to power split-second decision-making comes with the possibility of transmission delays, critical lags in ADAS functionality, and even data breaches, all of which jeopardise safety.

Instead, AV safety lies in empowering computer processing within the vehicle itself. To do so, the issue of computing constraints must be overcome, meaning models must be tailored specifically to the edge devices they’re running on. These smaller, more efficient models can help development teams unlock every bit of computational power that edge devices have to offer in order to achieve optimal speed and accuracy.

Smaller models, greater safety

The more automotive developers shrink and tailor the models inside automotive hardware, the closer the industry moves towards total vehicle autonomy. Right now, there are many ADAS safety capabilities that stand to benefit from more tailored models, such as object detection—computer vision systems that give AVs the potential to instantaneously recognise and react to objects on the road including hazards. With more efficient models, ADAS sensors can quickly process complex visual data in real-time to enable collision avoidance and automatic emergency braking.

ZF ProAIAccurate real-time data processing is essential for autonomous applications

Smaller, efficient models also stand to enhance pose estimation. By analysing posture, head orientation and eye gaze of the driver, pose estimation models are poised to detect fatigue and distraction in real-time, preventing avoidable accidents by assessing attention and alertness of the person behind the wheel. If such signs are detected, this ADAS feature can eventually prompt vehicles to automatically assume command momentarily while the driver regains attention.

Localisation and mapping also receive critical enhancements through improved models. With the implementation of advanced remote sensing methods like LiDAR, ADAS systems can determine a vehicle’s position and map its surroundings. However, this feature requires a vast amount of accurate real-time data processing for autonomous applications.

Lane tracking is another of the several key ADAS capabilities that stand to improve from tailored models. Processing the visual data from lane markings is a continuous task that requires lightning-fast and accurate analysis for complete autonomous functionality. With these capacities, ADAS could take far more pressure off long-distance drivers by helping to keep vehicles from straying from their lanes.

The make and model

Many other automotive developments—in-vehicle infotainment, cyber security, energy management—are likely to focus their solutions around edge computing, where data processing occurs in the vehicle itself rather than being carried out in the cloud. Therefore, it becomes critical that deep learning models are not just accurate but also rapid and efficient to support the range of functions needed for AVs to become widely adopted.

Developers should aim for deep learning models that are compact and designed specifically for certain hardware architectures. To that end, dev teams must optimise efficiency rates so that the models are able to fully leverage available computational resources and memory usage of the edge devices they run on—ADAS, onboard computers, and telematic devices, among others.

In short, tailoring smaller models to specific hardware will help automotive developers cross the autonomous finish line.

About the author: Yonatan Geifman is Chief Executive and Co-founder of Deci

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Hero DoorDash driver helps woman escape 'aggressor' only to be hunted down in terrifying car chase - New York Post

Hero DoorDash driver helps woman escape 'aggressor' only to be hunted down in terrifying car chase  New York Post
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Platform services are driving the future of automotive - Automotive World

Data-driven insights and personalised offers can be invaluable in delivering a stand-out experience, writes Jag Gill

In the 2023 American Financial Services Association Vehicle Finance Board Annual Survey, participants named the biggest opportunities as technology improvements, both at the enterprise and functional level, improved data management and the use of AI and machine learning. This is no surprise. The industry is increasingly adopting new technology to increase efficiency and agility and to maximise profitability in a difficult season. Companies that have embraced technologies such as predictive analytics, straight-through processing and integrated design are already reaping the benefits.

However, there is a major block for companies looking to gain from these technologies. Monolithic architectures are proving to be obstacles in the face of evolving market demands. Businesses need more agile back-office systems and the existing inflexible systems are holding back too many businesses.

In a competitive market, customer experience can be the differentiator between staying ahead or falling behind. Manufacturers that deliver exceptional customer experiences are proven to stand out from the crowd, and data-driven insights and personalised offers can be invaluable in delivering that stand-out experience.

Digital transformation

Microservices, a decentralised platform approach to delivering technology value, are typically developed to meet the needs of rapidly evolving markets like the automotive industry. Microservices are precisely segmented processes adopted to replace large legacy applications like those currently holding back automotive businesses from meeting the customers’ needs—and their business potential.

Seamless integration of consumer data through microservices delivers greater understanding of customer needs and preferences, because consumer data and insights are easier to manage, assess and maintain when they are broken down into smaller, composable pieces. These smaller pieces can be worked seamlessly together and provide a comprehensive customer view which is greater than the sum of the constituent and interactive parts. With greater insights, comes more tailored offers and ultimately a better customer experience and increased loyalty.

Integration of consumer data through microservices delivers greater understanding of customer needs and preferences

In addition, digitalisation via these decentralised platforms enables greater agility and responsiveness, faster time-to-market, real-time insights and improved customer experiences across the automotive industry. Cloud innovation and more flexible tech solutions also drive down long-term structural IT costs whilst increasing scalability and competitive advantage.

Dismantling the legacy

Seeking to streamline and modernise, a growing number of automotive businesses are beginning to dismantle their legacy systems and monolithic cores, in favour of more loosely coupled and manageable components. They are particularly focused on delivering responsive offers, alternative deal structures and defining new business and product models.

By way of comparison and inspiration it could be worth looking at the way modern banking and financial services’ architectures and real-time data have become key lynchpins to success. Top performers are now scrutinising the value of every customer, enabling analytics teams to increase revenue by incorporating disparate smart insights and data sources via digital decisioning. This delivers a far more comprehensive picture of the potential offered by each customer, and highlights which deals, add-ons and extras will help ensure they remain loyal.

 

The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.

Jag Gill is Senior Associate Partner at FICO

The AutomotiveWorld.com Comment column is open to automotive industry decision makers and influencers. If you would like to contribute a Comment article, please contact This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

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Freudenberg Sealing lands 'multiple major orders' for new automotive part - European Rubber Journal

29 Feb 2024

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Ecarx Outpaces Nvidia in Automotive Tech Race, Targets Global Market Expansion - BNN Breaking

In a significant shift within the automotive technology industry, Ecarx, a company founded by Geely's Eric Li, is making waves with its impressive financial achievements and strategic global market expansion plans. Backed by the Chinese automotive giant Geely, Ecarx has reported a remarkable 22% revenue increase to $263 million in its fourth quarter, notably outperforming Nvidia's 4% decline in automotive revenue. This surge highlights Ecarx's growing dominance in the digital car cockpit and driver assist systems market, challenging major players like Qualcomm and Nvidia.

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Rising Star in Automotive Tech

Ecarx's success is attributed to its focus on developing simpler, safety-focused driver assist technology, which addresses a broader segment of the automotive market than Nvidia's AI-based autonomous driving systems. CEO Ziyu Shen emphasizes the company's intent to unveil new products that will compete directly with Nvidia's Orin X, signaling Ecarx's ambition to not only lead in the Chinese market but also to significantly expand its global footprint. The company's strategy includes capitalizing on geopolitical tensions that limit its competitors, like Huawei, from entering global markets, thereby opening up substantial opportunities for Ecarx's overseas sales ambitions.

Strategic Moves and Market Expansion

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Ecarx's approach to market expansion is multifaceted, focusing on increasing its service offerings to local Chinese companies while also aggressively growing its overseas sales. Shen's goal is to boost international revenue from 10% to at least 25% next year, a move that could redefine the company's market position and set new standards in the automotive tech industry. This strategy leverages Ecarx's competitive edge in digital cockpit solutions and driver assist systems, sectors where the company has already shown significant innovation and growth. Moreover, Ecarx's ability to forge strategic collaborations and partnerships, as evidenced by their recent financial reports, underscores its potential for sustained growth and industry leadership.

Challenges and Opportunities Ahead

Despite its impressive trajectory, Ecarx faces challenges, including maintaining its growth amid potential global economic fluctuations and navigating the complex web of international trade and technology sanctions. However, the company's strategic positioning, coupled with its focus on innovation and market expansion, places it in a strong position to overcome these obstacles. As Ecarx continues to push the boundaries of automotive technology, its journey represents a fascinating case study in leveraging strategic market opportunities, technological innovation, and global expansion to challenge established industry giants.

The rise of Ecarx in the automotive tech industry underscores a significant shift towards more integrated, innovative, and safety-focused driver assistance systems. With its sights set on expanding its global market share, Ecarx is not just challenging the status quo but is also paving the way for the future of automotive technology. As the company continues to grow and evolve, its impact on the industry and on global technology landscapes will undoubtedly be a development worth watching.

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