The automotive industry is undergoing major changes
The change of pace in the industry means automakers need to invest urgently and wisely in strategic autonomous driving vehicle initiatives or they will not capitalize on this important market transformation. Gartner predicts “many of the most powerful changes expected in the auto industry will happen in the next 2 to 5 years.” And, “longer term, electric vehicle technologies and autonomous vehicle technologies will dominate the transformation of the auto industry.”1
Thus, competition in the autonomous vehicle space is heating up, drawing players from other industries, including software technology giants. Many automakers are partnering together or directly with technology and Silicon Valley-based vendors. For example, Chrysler is partnering with Waymo, General Motors with Honda, and Audi with Nvidia. Technology vendors getting into the autonomous driving race include Apple, Cisco, Intel, and Microsoft.
The right software platform is critical to R&D efforts
To provide real-time, no-fail safety and mobility, autonomous driving vehicles need highly secure and reliable networks, applications, and data analytics—all of which are powered by software. Automakers must make appropriate investments in a variety of technologies, and the underlying development platform is vital to this investment strategy.
With the goal of quickly putting fully autonomous vehicles on the road, automakers are hard at work on the research and development (R&D) phase. Data scientists and engineers are collecting, sorting, analyzing, interpreting, simulating, and iterating on a vast amount of data for a vehicle to navigate the road accurately without human input. Supporting the development process with the right IT infrastructure is essential.
IT’s role in autonomous driving R&D
For long-term success, an automaker’s autonomous driving R&D IT platform needs to be:
- Open source for ultimate extensibility and integration with state-of-the-art application services and technologies, including artificial intelligence (AI) and machine learning.
- Agile, scalable, and robust to accommodate the volume of applications and data needed to drive the analytics for autonomous vehicles, from R&D to pilot projects and commercial adoption.
- Optimized to orchestrate the analytics, machine learning, and simulation workloads flexibly and at scale based on resource requirements, including graphics processing unit (GPU), central processing unit (CPU), memory, and storage.
- Highly secure to prevent hacking that compromises the integrity of driving and safety features.
- Elastic so that it can adapt to workload changes and foster collaboration, innovation, and performance.