Safeguarding the trajectory planning algorithms of autonomous vehicles is crucial for safe operation in mixed traffic scenarios. This paper proposes the use of an ensemble of neural networks to work together under the moniker of “Crash Prediction Networks”. The system comprises multiple independent networks, each focusing on a different subset of sensory inputs. The aim is for the networks to work together in unison to reach a consensus of whether a vehicle might enter a catastrophic state to trigger an appropriate intervention. The proposed approach would act as an additional layer of safety by supervising the decision making module of an autonomous vehicle.

Though the proposed approach encompasses all the sensors and allied paraphernalia, the scope of this paper is exclusively limited to exploring safety monitors for visual sensors. The approach can, however, be extrapolated to other sensors. The evaluation was conducted using the CARLA simulator for simple driving scenarios studying the benefits of modeling temporal features to capture the motion in the environment. Additionally, the paper studies the importance of ‘accounting for uncertainty’in models dealing with vehicle safety.