Balancing Cost and Reliability in Autonomous Machine Design
As millions of self-driving cars are expected on the roads by 2025 and autonomous drones continue to generate billions in revenue, ensuring the safety and reliability of these machines has become a key concern for consumers, manufacturers, and regulators. However, the measures needed to safeguard autonomous hardware and software against malfunctions, cyberattacks, and other failures often drive up costs, impacting performance, energy efficiency, and weight, largely due to the use of semiconductor chips.
Researchers from the University of Rochester, Georgia Tech, and the Shenzhen Institute of Artificial Intelligence and Robotics for Society argue that the current trade-off between protection and cost stems from a “one-size-fits-all” approach to safeguarding these systems. In a recent paper published in , they propose a new adaptive protection model that tailors security measures to different parts of the system, optimizing reliability without disproportionately increasing costs.
