Autonomous vehicles rely on prediction modules, in order to plan collision-free trajectories. Vehicle trajectory prediction models are multimodal, to account for the multiple route options and the inherent uncertainty in human behavior. The state-of-the-art prediction models are deep-learning models, which are susceptible to mode collapse, a phenomenon in which the model fails to output the full distribution of modes and only predicts the most likely one. Mode collapsing poses safety concerns for autonomous driving, as missed predictions could result in collisions. Most works have focused on addressing this issue by generating diverse predictions that cover various route options at the environmental level. However, there are no metrics for mode-collapse. Furthermore, little attention has been given to generating diversity in the interaction modes among agent trajectories. Additionally, the traditional distance-based metrics are heavily dependent on datasets and do not evaluate interactions between agents. To this end, we propose a novel evaluation framework that assesses the interaction modes of joint trajectory predictions, focusing only on the safety-critical interactions in a dataset. We introduce a metric for mode-collapse and time-based metrics for mode correctness and coverage, shedding light on the temporal dimension of the predictions. We test four multi-agent trajectory prediction models on the widely used nuScenes dataset and conclude that mode collapse happens. While the rate of correctly predicted interaction modes increases closer to the interaction event, there are still cases where the models are unable to predict the interaction mode even right before the interaction happens. With the introduction of our novel framework, researchers can now benchmark their models’ performance in predicting critical interactions. This provides new insights and perspectives, helping the holistic evaluation and interpretation of a model’s performance. Additionally, our work offers a new developmental direction for prediction models, aiming for greater consistency and accuracy in predicting agent interactions, thereby advancing the safety of autonomous driving systems. Our evaluation framework is available online at: https://github.com/MaartenHugenholtz/InteractionEval

Autonomy in traffic (e.g., autonomous vehicles) could potentially benefit mobility, safety, accessibility and sustainability. However, the realisation of these advancements is highly dependent on how effective these autonomous vehicles interact with vulnerable road users such as pedestrians. Before we can understand how pedestrians will interact with autonomous vehicles, it is essential to understand how pedestrians interact among themselves in interactive traffic scenarios. Previous studies have focused on describing these scenarios with probabilistic trajectory prediction methods such as TrajFlow. However, these approaches often fall short in capturing the nuances of mutual interactions. Simple interaction models have been proposed that can describe these interactions, but neglect the influence of another person's intentions. To address this issue, in existing work the Communication-Enabled-Interaction (CEI) framework was proposed that describes interactions by modelling communication and a belief of another person's intentions. The idea of using beliefs in interaction modelling is based on the concept that people have a general but uncertain idea about the plans of other people. These beliefs are one of the fundamental aspects of the CEI framework and must therefore contain valuable information about possible decisions. That is why this study investigates the use of the probabilistic trajectory prediction method TrajFlow for the belief construction of the CEI framework. TrajFlow is trained on the belief-based Forking Paths dataset, integrated into the CEI framework, and tested in four simulated pedestrian interaction scenarios. The analysis shows that the framework is able to simulate plausible interaction behaviour, dealing with conflicting goals and trajectories in multiple simulations. By doing so, this study takes a positive step towards modelling pedestrian interactions and contributes to the broader goal of realising the benefits linked to autonomy in traffic.

A widely held assumption within the field of Trajectory Prediction is the perfect and complete observation of agents’ pasts. While this assumption allows for a simpler representation of the prediction problem, it no longer holds true when prediction models are expected to operate on histories generated by upstream perception systems, which are susceptible to fail. Occlusions are a particularly important cause of perception failures. They fragment tracked agents’ trajectories, and can often hide their most recent position(s) from the perception system. While most prediction models that are currently being researched cannot account for the possible incompleteness of agents’ past histories, we devise a prediction model that is designed to directly operate on partially missing histories caused by occlusions. We present OcclusionFormer, a TransFormer based prediction model, which predicts agent’s futures from their last observed position, without requiring imputation of missing past positions. Experiments show that our design is occlusion capable, as it can predict from trajectories with partially missing data, while remaining performant in the ideal, fully observed scenario. We also conduct research on the integration of an occlusion map within our model, which could help narrow the region of plausible prediction for occluded agents. We observe that, while the addition of such a map does improve the coherence of predictions with respect to the configuration of the occluded space, it results in a degradation of prediction performance.