At the 2016 International Consumer Electronics Show (CES), the growing interest in autonomous driving technology was clearly evident. In the chip market for self-driving vehicles, well-known players like Nvidia, Mobileye, NXP, and Texas Instruments were joined by new entrants such as Ceva, Intel, and Qualcomm. Automotive OEMs are increasingly welcoming these newcomers into the space, with Egil Juliussen, a researcher at IHS Automotive, noting that “this area has suddenly become very lively.â€
The concept of a “fog of war†has emerged in this rapidly evolving industry. While investors and media have long been excited about autonomous vehicle technologies—ranging from sensors, cameras, radar, and lidar to mapping, algorithms, and deep learning—it remains unclear how these technologies will shape the future of autonomous car design or who will ultimately succeed.
Mobileye’s co-founder and CEO, Amnon Shashua, initially thought some competitors were spreading misinformation to create confusion. But he now believes the uncertainty is genuine: “People are really confused because they don’t understand.â€
At CES 2016, Nvidia’s “deep learning†technology and Mobileye’s mapping solutions stood out. The two companies competed fiercely in the ADAS and autonomous driving space. During a press conference, Shashua criticized Nvidia, pointing out that its 250W supercomputer costs nearly $10,000, which he said doesn’t belong in the automotive world. Ceva’s CEO, Gideon Wertheizer, described the rivalry as “a bridge of investment,†noting that Mobileye’s stock dropped after Nvidia’s announcement but rebounded following the press event.
Mobileye’s Road Experience Management System (REM) is one of the most innovative mapping technologies in the field. It enables real-time, multi-source data collection for precise positioning and high-resolution lane data, essential for full automation. This system uses software integrated with Mobileye’s EyeQ chips to process low-bandwidth data—about 10KB per kilometer of travel. The cloud-based back-end then aggregates this data from all connected vehicles to build a global map.
However, REM only works on cars equipped with Mobileye’s EyeQ chips, effectively locking in customers. Shashua emphasized that automakers can leverage their scale to create their own road guides using REM, and that the system is easy to implement with existing EyeQ chips and communication links like GM’s On-Star.
GM and Volkswagen announced support for REM at CES, and more major automakers are expected to follow. Shashua noted that one-third of the global automotive industry already uses EyeQ chips, and he expects REM to be widely adopted across the industry soon.
In contrast, Nvidia, led by CEO Huang Renxun, promotes a vision centered on deep learning and sensor fusion. Its Drive PX 2 platform is designed as a “supercomputer for cars,†capable of processing data from multiple sensors—including cameras, radar, and lidar—to detect objects, determine location, and plan safe paths.
Nvidia also introduced its Digits platform, which helps automakers develop and train deep neural networks faster. According to Huang, autonomous driving is not just about programming a car to drive, but about building end-to-end systems—from neural network training to in-vehicle processing.
Ceva and Qualcomm are also making their mark. Ceva’s XM4 imaging and visual DSPs, combined with its CDNN architecture, enable embedded systems to perform deep learning tasks faster and with less power than GPU-based solutions. Qualcomm’s Snapdragon 820A includes LTE modems and machine intelligence features, targeting both infotainment and ADAS systems.
Shashua highlighted that while there are two main approaches to autonomous driving—highly detailed maps versus sensor-enhanced local maps—Mobileye focuses on the latter, believing it is more scalable and practical for the automotive industry. He sees REM as a key step toward creating a global, cloud-based map that can be updated in real time.
Ultimately, Shashua argues that the battle for autonomous vehicles isn’t just about chip architecture, but about software and content. As the industry evolves, the competition between companies like Nvidia, Mobileye, and others will continue to shape the future of self-driving technology.
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