In the race to manufacture autonomous vehicles (AVs), safety is crucial yet sometimes overlooked as exemplified by recent headline-making accidents. Researchers are using artificial intelligence (AI) and machine learning to improve the safety of autonomous technology through both software and hardware advances.

«Using AI to improve autonomous vehicles is extremely hard because of the complexity of the vehicle’s electrical and mechanical components, as well as variability in external conditions, such as weather, road conditions, topography, traffic patterns, and lighting,» said Ravi Iyer

«Progress is being made, but safety continues to be a significant concern.»

The group has developed a platform that enables companies to more quickly and cost-effectively address safety in the complex and ever-changing environment of autonomous technology. They are collaborating with many companies in the Bay area, including Samsung, NVIDIA, and a number of start-ups.

«We are seeing a stakeholder-wide effort across industries and universities with hundreds of startups and research teams, and are tackling a few challenges in our group,» said Saurabh Jha, a doctoral candidate in computer science who is leading student efforts on the project. «Solving this challenge requires a multidisciplinary effort across science, technology, and manufacturing.»

One reason this work is so challenging is that AVs are complex systems that use AI and machine learning to integrate mechanical, electronic, and computing technologies to make real-time driving decisions. A typical AV is a mini-supercomputer on wheels; they have more than 50 processors and accelerators running more than 100 million lines of code to support computer vision, planning, and other machine learning tasks.

Story Source: Materials provided by University of Illinois College of Engineering. Note: Content may be edited for style and length.