Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment
This work addresses pedestrian safety for autonomous vehicles by providing a comprehensive benchmark, but it is incremental as it builds on existing datasets and models without introducing new methods.
The paper tackled pedestrian behavior understanding by defining and benchmarking three tasks—intention estimation, action prediction, and event risk assessment—using datasets JAAD and PIE, and applied new metrics to evaluate four SOTA models, revealing insights into data modalities and task complementarity.
In this paper, we delve into the pedestrian behavior understanding problem from the perspective of three different tasks: intention estimation, action prediction, and event risk assessment. We first define the tasks and discuss how these tasks are represented and annotated in two widely used pedestrian datasets, JAAD and PIE. We then propose a new benchmark based on these definitions, available annotations, and three new classes of metrics, each designed to assess different aspects of the model performance. We apply the new evaluation approach to examine four SOTA prediction models on each task and compare their performance w.r.t. metrics and input modalities. In particular, we analyze the differences between intention estimation and action prediction tasks by considering various scenarios and contextual factors. Lastly, we examine model agreement across these two tasks to show their complementary role. The proposed benchmark reveals new facts about the role of different data modalities, the tasks, and relevant data properties. We conclude by elaborating on our findings and proposing future research directions.