Co-Design of Out-of-Distribution Detectors for Autonomous Emergency
  Braking Systems
Michael Yuhas Arvind Easwaran
Learning enabled components (LECs), while critical for decision making in
autonomous vehicles (AVs), are likely to make incorrect decisions when
presented with samples outside of their training distributions.
Out-of-distribution (OOD) detectors have been proposed to detect such samples,
thereby acting as a safety monitor, however, both OOD detectors and LECs
require heavy utilization of embedded hardware typically found in AVs. For both
components, there is a tradeoff between non-functional and functional
performance, and both impact a vehicle's safety. For instance, giving an OOD
detector a longer response time can increase its accuracy at the expense of the
LEC. We consider an LEC with binary output like an autonomous emergency braking
system (AEBS) and use risk, the combination of severity and occurrence of a
failure, to model the effect of both components' design parameters on each
other's functional and non-functional performance, as well as their impact on
system safety. We formulate a co-design methodology that uses this risk model
to find the design parameters for an OOD detector and LEC that decrease risk
below that of the baseline system and demonstrate it on a vision based AEBS.
Using our methodology, we achieve a 42.3% risk reduction while maintaining
equivalent resource utilization.