  Understanding novel situations in the traffic domain requires an intricate
combination of domain-specific and causal commonsense knowledge. Prior work has
provided sufficient perception-based modalities for traffic monitoring, in this
paper, we focus on a complementary research aspect of Intelligent
Transportation: traffic understanding. We scope our study to text-based methods
and datasets given the abundant commonsense knowledge that can be extracted
using language models from large corpus and knowledge graphs. We adopt three
knowledge-driven approaches for zero-shot QA over traffic situations, based on
prior natural language inference methods, commonsense models with knowledge
graph self-supervision, and dense retriever-based models. We constructed two
text-based multiple-choice question answering sets: BDD-QA for evaluating
causal reasoning in the traffic domain and HDT-QA for measuring the possession
of domain knowledge akin to human driving license tests. Among the methods,
Unified-QA reaches the best performance on the BDD-QA dataset with the
adaptation of multiple formats of question answers. Language models trained
with inference information and commonsense knowledge are also good at
predicting the cause and effect in the traffic domain but perform badly at
answering human-driving QA sets. For such sets, DPR+Unified-QA performs the
best due to its efficient knowledge extraction.
