  Intelligent Traffic Monitoring (ITMo) technologies hold the potential for
improving road safety/security and for enabling smart city infrastructure.
Understanding traffic situations requires a complex fusion of perceptual
information with domain-specific and causal commonsense knowledge. Whereas
prior work has provided benchmarks and methods for traffic monitoring, it
remains unclear whether models can effectively align these information sources
and reason in novel scenarios. To address this assessment gap, we devise three
novel text-based tasks for situational reasoning in the traffic domain: i)
BDD-QA, which evaluates the ability of Language Models (LMs) to perform
situational decision-making, ii) TV-QA, which assesses LMs' abilities to reason
about complex event causality, and iii) HDT-QA, which evaluates the ability of
models to solve human driving exams. We adopt four knowledge-enhanced methods
that have shown generalization capability across language reasoning tasks in
prior work, based on natural language inference, commonsense knowledge-graph
self-supervision, multi-QA joint training, and dense retrieval of domain
information. We associate each method with a relevant knowledge source,
including knowledge graphs, relevant benchmarks, and driving manuals. In
extensive experiments, we benchmark various knowledge-aware methods against the
three datasets, under zero-shot evaluation; we provide in-depth analyses of
model performance on data partitions and examine model predictions
categorically, to yield useful insights on traffic understanding, given
different background knowledge and reasoning strategies.

Computation and Language;Artificial Intelligence;Machine Learning