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BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving (2312.06371v2)

Published 11 Dec 2023 in cs.RO and cs.AI

Abstract: The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available at https://github.com/Petrichor625/BATraj-Behavior-aware-Model.

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Citations (23)
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Summary

  • The paper introduces the BAT model that fuses behavior, interaction, priority, and position modules to significantly improve trajectory prediction accuracy.
  • It employs continuous behavioral representation and polar coordinate modeling to capture complex interactions without relying on manual labeling.
  • Empirical evaluations across four datasets demonstrate BAT’s robustness, achieving competitive results even with minimal training data.

Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

Autonomous driving technology is progressing rapidly, yet the precise prediction of vehicle trajectories remains a pivotal challenge. The paper "BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving" introduces a novel model - the Behavior-Aware Trajectory (BAT) prediction model - which seeks to enhance accuracy and reliability in predicting the future trajectories of surrounding vehicles by drawing upon insights from human driving behaviors, traffic psychology, and decision-making processes. This paper presents an exploration into the integration of behavior, interaction, priority, and position awareness into trajectory prediction, highlighting the model's adaptability, efficiency, and competitive performance compared to state-of-the-art benchmarks.

The BAT model is structured around four modules: behavior-aware, interaction-aware, priority-aware, and position-aware modules. The behavior-aware module leverages centrality metrics from dynamic geometric graphs to interpret and evaluate human driving behavior. By circumventing discrete behavior categories in favor of continuous behavioral representation, this module enhances prediction accuracy without relying on manual labeling. The interaction-aware pooling mechanism accounts for higher-order interactions, capturing agent displacements via polar coordinates, which proves advantageous over traditional Cartesian coordinates in handling complex, non-linear driving scenarios. The priority-aware module employs an attention mechanism to assign importance weights to surrounding vehicles, while the position-aware module uses an LSTM network to encode the ego vehicle's dynamic positioning. Combined, these components create a comprehensive model that outputs probabilistic trajectory distributions via a Bayesian hierarchical framework conditioned on potential maneuvers.

Quantitative evaluation of the BAT model was carried out using four publicly available datasets: NGSIM, HighD, RounD, and the newly introduced Macao Connected Autonomous Driving (MoCAD) dataset. The model consistently outperforms existing benchmarks, notably maintaining commendable accuracy even when trained on just 25% of the dataset. Such results underscore BAT's robustness and efficiency in learning and its potential to effectively handle corner cases or reduce data requirements for autonomous vehicle training.

The comprehensive ablation studies delineate the contributions of each module, emphasizing improvements in trajectory prediction accuracy afforded by behavior-aware pooling and polar coordinate modeling. Notably, insights from traffic psychology and decision theory amalgamated into behavior-aware modeling featured prominently in reducing RMSE compared to traditional methods that lacked such intricacies.

Practically, the implications of this research are substantial for autonomous driving systems that demand precision in predicting vehicular actions in dynamic environments such as busy intersections and roundabouts. Furthermore, adopting human-centric spatial perception methodologies reinforces the model's credibility and application to real-world driving scenarios.

Theoretically, the paper pushes the frontier of trajectory prediction by intertwining human behavior analysis with machine learning, thus paving the way for future developments where human-like judgment is embedded seamlessly within autonomous vehicle systems. This integration potentially facilitates the evolution of driver assistance and traffic management systems that can dynamically adapt to varied environments and driving behaviors.

In conclusion, the BAT model represents a significant advancement in trajectory prediction for autonomous vehicles. Its robust framework, underpinned by traffic psychology and continuous behavioral insights, offers a promising avenue for driving autonomy closer to human-like comprehension and response capabilities, ensuring safer and more intelligent interaction in complex driving environments. Future research could explore further enhancements in behavior-aware modeling, perhaps by integrating more nuanced traffic psychology paradigms, ensuring autonomous vehicles improve their interaction and prediction accuracy across diverse global contexts.

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  1. GitHub - Petrichor625/BATraj-Behavior-aware-Model (104 stars)