Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

A novel compact deep multi-task learning model is introduced for autonomous driving perception, capable of handling multiple tasks in a single forward pass. This model simultaneously performs semantic segmentation, depth estimation, LiDAR segmentation, and bird's eye view projection. It incorporates an adaptive loss weighting algorithm to address imbalanced learning issues arising from numerous tasks. Through data pre-processing and intermediate sensor fusion techniques, the model integrates input modalities from RGB cameras, dynamic vision sensors (DVS), and LiDAR sensors positioned on the ego vehicle, enhancing environmental understanding. Ablation and comparative studies demonstrate that the proposed method achieves superior performance with significantly fewer parameters, enabling faster inference and reduced GPU memory utilization. The model's consistency is validated across three CARLA simulation datasets and one real-world nuScenes-lidarseg dataset.

Key takeaway

For Machine Learning Engineers developing autonomous driving perception systems, this research offers a path to significantly reduce computational overhead. You should consider integrating compact multi-task learning models that leverage adaptive loss weighting and intermediate multi-sensor fusion. This approach allows for faster inference and lower GPU memory usage, crucial for deploying robust perception capabilities on resource-constrained edge devices in real-world scenarios.

Key insights

The model integrates multi-sensor data and multi-task learning into a compact architecture for efficient autonomous driving perception.

Principles

• Compact multi-task models reduce inference overhead.
• Adaptive loss weighting improves imbalanced learning.
• Multi-sensor fusion enhances environmental understanding.

Method

The model uses data pre-processing and intermediate sensor fusion to combine RGB, DVS, and LiDAR inputs, then applies an adaptive loss weighting algorithm for multi-task learning.

In practice

• Implement compact multi-task perception.
• Integrate RGB, DVS, and LiDAR data.
• Utilize adaptive loss weighting for task balance.

Topics

• Autonomous Driving
• Multi-task Learning
• Sensor Fusion
• Semantic Segmentation
• LiDAR Perception
• Deep Learning Models

Code references

• oskarnatan/compact-perception

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.