A safety-assured architecture for AI-enabled autonomous vehicles

点击翻译按钮获取中文摘要

Artificial intelligence (AI) has enabled new kinds of autonomous mobile robots that are on the verge of transforming multiple sectors of society. These include autonomous vehicles (AVs), sidewalk delivery robots, autonomous agricultural machines, cleaning robots, autonomous trains, and drones. Today, many of these robots fundamentally rely on deep learning for perception tasks such as object recognition, but deep learning starts to also penetrate prediction, planning, and control tasks. These advances greatly improve the adaptability of these systems and their ability to handle complex situations and interactions. These systems are expected to interact with humans and other robots in open environments that are subject to unexpected obstacles, varying weather conditions, and evolving infrastructures. Unfortunately, systems that rely on deep learning pose a great challenge to safety assurance, which is a major obstacle to their wide deployment. Deep neural networks (DNNs) lack human interpretability and may fail in unpredictable ways on new inputs. Further, the tasks they target, such as recognizing a pedestrian, evade complete specifications. These properties make the application of traditional safety assurance methods to these systems difficult or impossible. This Discovery grant will fund a cutting-edge research program to create the next generation of system architectures for AVs that take advantage of modern AI-technologies but are inherently assurable. The expected results include (1) an architecture for perception and planning that is inspired by the dual-process theory of human cognition and integrates the high-performance decision-making of DNNs with safety-relevant reasoning in interpretable, symbolic form; and (2) the methods to assure the safety of robots using such an architecture. The program will use the UW Moose, an AV developed by the PI's team, to demonstrate and evaluate the new technology on public roads. AVs and other mobile robots will have a major impact on our society and economy over the coming decades. It is expected that AVs can reduce accident rates by as much as 80%. In Canada, this would save 1,600 lives a year and over $55 billion in healthcare costs and lost productivity. The proposed research program targets current major roadblock to a wider adoption of such systems, which is assuring their safety. The program will generate cutting-edge research results in AI and robotics, share datasets and benchmarks to stimulate scientific and technological progress, create commercializable technology, and train 24 HQP in AI and robotics over the next five years. Hands-on experience on an AV will give these HQP a significant competitive advantage on the job market. The automotive sector is the single biggest contributor to Canada's manufacturing GDP. Canadian innovation and HQP training in AV technology will contribute to maintaining Canada's role as a world leader in the automotive sector and AI.