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Despite the successful demonstration of autonomous vehicles (AVs), such asself-driving cars, ensuring AV safety remains a challenging task. Although someactors influence an AV's driving decisions more than others, current approachespay equal attention to each actor on the road. An actor's influence on the AV'sdecision can be characterized in terms of its ability to decrease the number ofsafe navigational choices for the AV. In this work, we propose a safety threatindicator (STI) using counterfactual reasoning to estimate the importance ofeach actor on the road with respect to its influence on the AV's safety. We usethis indicator to (i) characterize the existing real-world datasets to identifyrare hazardous scenarios as well as the poor performance of existingcontrollers in such scenarios; and (ii) design an RL based safety mitigationcontroller to proactively mitigate the safety hazards those actors pose to theAV. Our approach reduces the accident rate for the state-of-the-art AV agent(s)in rare hazardous scenarios by more than 70%.

Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards