HiLO: High-Level Object Fusion for Autonomous Driving using Transformers
This work addresses the need for robust and computationally efficient perception systems in near-production autonomous vehicles, representing an incremental advancement over traditional methods like the Kalman filter.
The paper tackled the problem of sensor data fusion for autonomous driving by proposing HiLO, a transformer-based high-level object fusion method, which achieved improvements of 25.9 percentage points in F1 score and 6.1 percentage points in mean IoU.
The fusion of sensor data is essential for a robust perception of the environment in autonomous driving. Learning-based fusion approaches mainly use feature-level fusion to achieve high performance, but their complexity and hardware requirements limit their applicability in near-production vehicles. High-level fusion methods offer robustness with lower computational requirements. Traditional methods, such as the Kalman filter, dominate this area. This paper modifies the Adapted Kalman Filter (AKF) and proposes a novel transformer-based high-level object fusion method called HiLO. Experimental results demonstrate improvements of $25.9$ percentage points in $\textrm{F}_1$ score and $6.1$ percentage points in mean IoU. Evaluation on a new large-scale real-world dataset demonstrates the effectiveness of the proposed approaches. Their generalizability is further validated by cross-domain evaluation between urban and highway scenarios. Code, data, and models are available at https://github.com/rst-tu-dortmund/HiLO .