Paper #2 – Scenario2Vector: Scenario Description Language Based Embeddings for Traffic Situations

19 May 2021
3:00 - 3:15 AEST
12:00 pm - 12:15 pm CDT
19:00 - 19:15 CEST
17:00 - 17:15 UTC
Online

Paper #2 – Scenario2Vector: Scenario Description Language Based Embeddings for Traffic Situations

Scenario2Vector: scenario description language based embeddings for traffic situations

  • Aron Harder
  • Jaspreet Ranjit
  • Madhur Behl

A popular metric for measuring progress in autonomous driving has been the “miles per intervention”. This is nowhere near a sufficient metric and it does not allow for a fair comparison between the capabilities of two autonomous vehicles (AVs). In this paper we propose Scenario2Vector – a Scenario Description Language (SDL) based embedding for traffic situations that allows us to automatically search for similar traffic situations from large AV data-sets. Our SDL embedding distills a traffic situation experienced by an AV into its canonical components – actors, actions, and the traffic scene. We can then use this embedding to evaluate similarity of different traffic situations in vector space. We have also created a first of its kind, Traffic Scenario Similarity (TSS) dataset which contains human ranking annotations for the similarity between traffic scenarios. Using the TSS data, we compare our SDL embedding -with textual caption based search methods such as Sentence2Vector. We find that Scenario2Vector outperforms Sentence2Vector by 13% ; and is a promising step towards enabling fair comparisons among AVs by inspecting how they perform in similar traffic situations. We hope that Scenario2Vector can have a similar impact to the AV community that Word2Vec/Sent2Vec have had in Natural Language Processing datasets.