UC San Diego

Autonomous driving systems involved in perception and planning require large volumes of carefully annotated data for learning and validation. These same systems also must be aware of failure cases so that they can safely request and initiate control transitions to human drivers or remote operators. In this dissertation, I present novelty detection as a unifying solution to both of these problems. Through novelty detection, active learning algorithms can reduce annotation costs by intelligently selecting informative data, which I demonstrate on tasks of 3D object detection and vehicle trajectory prediction. Similarly, novelty detection acts as a requisite step for safely handling hazardous scenarios. Lastly, I present the concept of salience as a property of road objects which expresses their criticality to control decisions, discussing the relevance of this property in developing machine learning systems which have stronger learning and validation over safety-critical scene elements for autonomous driving and can adapt to novelty found in the open world.