AUTOMOTIVE DESIGN
Fully autonomous vehicles could have up to a billion lines of code
ACCELERATING ADAS R
Where are we on the road to autonomous driving?
egulatory hurdles, semiconductor shortages, geopolitical disruption; these are just some of
the challenges currently facing the automotive industry. To adapt, many OEMs are rerouting investment to develop and deploy advanced driver assistance systems (ADAS) that are equipped with hands-off and eyes- off driving technologies for short- term returns. While ADAS may have begun
as a premium feature in executive cars, increasing interest from both consumers and safety regulators has driven a surge in adoption across all vehicle classes. As such, there has been a whole host of innovation in this area of late, from cameras and control units to radar and lidar systems, GPS and mapping data, to Human-Machine Interfaces (HMIs) and connectivity. In this article, two leading
developers of ADAS hardware, software and systems share their insights into the market and what they believe will be the next step on the path towards autonomous vehicles.
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INCREASING COMPLEXITY AND AI “With increasing levels of complexity in ADAS/AD systems, it is becoming nearly impossible to thoroughly test and validate designs with traditional methods,” says Emmanuel Follin, senior manager, product management at Ansys. The company’s Autonomous Vehicle Simulation software supports the development, testing and validation of safe automated driving technologies while saving time and money for OEMs. He continues: “An example of
this is physical road testing, which is extremely expensive and time- consuming. As a result of these obstacles, virtual validation via simulation is gaining more traction than ever before. Especially with higher levels of autonomy, trusted, high-fidelity simulation tools are becoming a mainstream component of the autonomous industry’s manufacturer’s toolbox.” According to Follin, another major
trend is incorporating AI into ADAS design. “AI can coordinate vehicle
connectivity, allowing vehicle information to be shared via high- speed wireless networks,” he explains. “AI also facilitates ADAS and AV perception using large amounts of data intelligence that is collected by sensors during drive time. This information guides system safety by improving system perception, helping ADAS vehicles respond to a variety of driving scenarios. One more area where AI/machine learning is making a profound impact is in the annotation of real-world driving maps and ground truth sensor data – this leads to robust perception outcomes and enhanced safety.” This insight is echoed by Suraj
Gajendra, vice president of products and solutions of Arm’s Automotive Line of Business. Over the past three decades, Arm has become a global computing platform, with more than 70% of the world’s population using products based on the company’s technology. Together with its automotive partner ecosystem, Arm provides OEMs with the processor IP, tools and software solutions for
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