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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 …
order to achieve robust and accurate scene understanding, autonomous vehicles are …
Real-time semantic map** for autonomous off-road navigation
In this paper we describe a semantic map** system for autonomous off-road driving with
an All-Terrain Vehicle (ATVs). The system's goal is to provide a richer representation of the …
an All-Terrain Vehicle (ATVs). The system's goal is to provide a richer representation of the …
[PDF][PDF] A Survey on Terrain Traversability Analysis for Autonomous Ground Vehicles: Methods, Sensors, and Challenges.
Understanding the terrain in the upcoming path of a ground robot is one of the most
challenging problems in field robotics. Terrain and traversability analysis is a …
challenging problems in field robotics. Terrain and traversability analysis is a …
[HTML][HTML] AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control
Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC
ideate a data-centered society aimed at improving efficiency by automating and optimizing …
ideate a data-centered society aimed at improving efficiency by automating and optimizing …
Camera–Lidar sensor fusion for drivable area detection in winter weather using convolutional neural networks
Autonomous vehicles rely on perceiving the road environment through a perception pipeline
fed by a variety of sensor modalities, including camera, lidar, radar, infrared, gated camera …
fed by a variety of sensor modalities, including camera, lidar, radar, infrared, gated camera …