Autonomous vehicles are about to change completely the transportation systems, the automotive and military markets, and burst deep space exploration. However, while autonomous cars are expected to reduce of two-three orders of magnitude the number of traffic accidents and burst space exploration, the current self-driving systems are not yet compliant with ISO26262 dependability requirements to be adopted in large-scale and are not yet sufficiently reliable to be part of a space mission. In particular object detection, a critical task in autonomous vehicles, has been demonstrated to be highly undependable and to be responsible for the great majority of accidents in current self-driving cars prototypes. “Pursuing Efficient Reliability of Object Detection for automotive and aerospace applications” (PERIOD) challenge is to improve the dependability of object detection frameworks in an effective and efficient way. PERIOD aims at analyzing and proposing solutions to overcome the software and hardware dependability issues of object detection. By correlating computing architectures and software reliability analyses with the impact of faults in the vehicle behavior, PERIOD aims at reducing the probability of misdetection without the time, power, and cost overheads that make traditional fault-tolerance solutions unsuitable for automotive or aerospace real-time systems. The proposed action will enable a highly interdisciplinary collaboration between the experienced researcher, a talented associate professor with a significant track record in computer science and computer engineering, and the supervisor, a world leader in test, embedded systems, and computing architectures for automotive/space applications whose group is embedded systems in one of Europe’s leading research institutions.