Sensors, Safety Models and A System-Level Approach to Safe and Scalable Automated Vehicles
This work addresses safety and scalability challenges for automated vehicles, but it appears incremental as it builds on existing system-level safety concepts without introducing a new paradigm.
The paper tackles the problem of evaluating sensor accuracy in automated vehicles by arguing that individual sensor performance must be assessed within the overall system design, using redundancy, diverse sensing modalities, and safety models to mitigate failures and ensure safety and scalability.
When considering the accuracy of sensors in an automated vehicle (AV), it is not sufficient to evaluate the performance of any given sensor in isolation. Rather, the performance of any individual sensor must be considered in the context of the overall system design. Techniques like redundancy and different sensing modalities can reduce the chances of a sensing failure. Additionally, the use of safety models is essential to understanding whether any particular sensing failure is relevant. Only when the entire system design is taken into account can one properly understand the meaning of safety-relevant sensing failures in an AV. In this paper, we will consider what should actually constitute a sensing failure, how safety models play an important role in mitigating potential failures, how a system-level approach to safety will deliver a safe and scalable AV, and what an acceptable sensing failure rate should be considering the full picture of an AV's architecture.