Grapevine

Balancing cost and reliability in autonomous machine design

With millions of self-driving cars projected to be on the road in 2025 and autonomous drones generating billions in annual sales, safety and reliability are important considerations for consumers, manufacturers, and regulators. But solutions for protecting autonomous machine hardware and software from malfunctions, attacks, and other failures also increase costs. Those costs arise from performance features, energy consumption, weight, and the use of semiconductor chips.

Researchers from the University of Rochester, Georgia Tech, and the Shenzen Institute of Artificial Intelligence and Robotics for Society say that the existing tradeoff between overhead and protecting machines against vulnerabilities is due to a "one-size-fits-all" approach to protection. In a paper published in Communications of the ACM, the authors propose a new approach that adapts to varying levels of vulnerabilities within an autonomous machine system to make them more reliable and control costs.

Yuhao Zhu, an associate professor in Rochester's Department of Computer Science, says one example of a current "one-size-fits-all" approach is Tesla's use of two Full Self-Driving Chips (FSD Chips) in each vehicle—a redundancy that provides protection in case the first chip fails but doubles the cost of chips for the car. By contrast, Zhu says he and his students have taken a more comprehensive approach to protect against both hardware and software vulnerabilities and more wisely allocate protection.

"The basic idea is that you apply different protection strategies to different parts of the system," says Zhu. "You can refine the approach based on the inherent characteristics of the software and hardware. We need to develop different protection strategies for the front end versus the back end of the software stack."

For example, Zhu says the front end of an autonomous vehicle's software stack is focused on sensing the environment through devices such as cameras and light detection and ranging (LiDAR), while the back end processes that information, plans the route, and sends commands to the actuator.