In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability.
Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.
By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.
With the feedback of the observed trajectory, the algorithm is applied to neural-network-based models to improve the performance of driving behavior predictions across different human subjects and scenarios.
In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed.
In fact, the limitation to certain scenario is mainly due to the lackness of generic representations of the environment.
The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method.
Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module.
Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted.
In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios.
The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents.
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles.
Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles.