Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of what to fuse, when to fuse, and how to fuse remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/.

Automated summary

Deep learning is driving recent advancements in perception for autonomous driving through the fusion of multiple sensors, but questions regarding network architecture design, fusion timing, and methods remain open. This review aims to systematically summarize methodologies for deep multi-modal object detection and semantic segmentation in autonomous driving, while also discussing challenges and open questions. The reviewed study provides an overview of the topic, fusion methodologies, and offers an interactive online platform for further exploration.