By mapping data and carefully navigating roads, driverless cars could create smarter and safer transportation. That is if this emerging technology can address concerns about reliability.
At Bloomberg’s Value of Data conference, sponsored by Western Digital and hosted in New York, Waze and a panel of experts in the driverless cars space discussed how to improve self-driving car functionality, security, and connectivity to reinvent the transportation industry. In this article, we highlight how more reliable autonomous vehicles are being made.
- Sam Anthony, Chief Technical Officer & Co-Founder, Perceptive Automata
- Wei Luo, Chief Operating Officer, DeepMap
- Raj Rajkumar, Professor of Electrical & Computer Engineering and Robotics Institute, Carnegie Mellon University
- Michael Nanopoulos, Data Scientist, Waze
- Gabrielle Coppola, Auto Industry Reporter, Bloomberg
How Driverless Cars Deal with Data Quality versus Quantity
To make real-time decisions on the road, driverless vehicles need consistent access to high-quality data. That data has to live at the edge, where sensors collect and analyze ever-changing road conditions. Some cars have already been making use of this advanced technology to take actions such as automatic braking, lane departure warning, and blind spot notification. In fact, a recent report conducted by Accenture Strategy and Western Digital concluded that installing advanced computers on all newly manufactured cars could save thousands of lives each year.
But, data processing shares a linear relationship with onboard computing power. That is, the higher the quality of images that driverless cars want to collect and analyze, the more processing power needed. And, since making real-time driving decisions would be too risky from the latency of cloud computing, the remaining solution is embedded storage in autonomous vehicles.
As Raj Rajkumar, Professor of Electrical & Computer Engineering and Robotics Institute, Carnegie Mellon University, made clear in the panel,
“The problem with self-driving vehicles is that if you get a high-resolution image and frame rate at 30 frames per second, you need to process every pixel. That’s going to be very demanding. You’re basically going to have high-powered GPUs, and that in turn requires a lot of power and a lot of cooling. So, the demands become overwhelming pretty quickly.” – Raj Rajkumar
Instead, startups in the driverless vehicle industry face the challenge of processing images that are high enough quality to make safe driving decisions, but not too compute-intensive. Striking this balance is among the most pressing reliability concerns. The key might be forming new partnerships between OEMs, Tier-1 suppliers, and autonomous vehicle companies.
Finding Data Sources for Driverless Cars
So, how can a car come to understand and react to road conditions better than a human? If sensors are an autonomous vehicle’s eyes and ears, then data is its beating heart. To achieve fully autonomous—known as level 51—vehicles, the streams of data that run through driverless cars need the right data infrastructure.
That’s why automotive industry disrupters such as Wei Luo, Chief Operating Officer at DeepMap, are starting at the source: automotive OEMs and Tier-1 suppliers. As Wei described during the panel,
“We partner with various OEMs and Tier-1 suppliers so that we can actually embed the software that we build directly as part of a self-driving software stack.” – Wei Luo
By building software solutions into autonomous vehicles, the belief is that data can be more efficiently collected and processed—as well as the context of road conditions that inform driving decisions. Doing so might play a huge part in addressing the reliability concerns of autonomous vehicles.
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