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Found 11 papers, 6 papers with code
Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps
Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents.
LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction
Semantic grids can be useful representations of the scene around an autonomous system.
Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks
This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'.
LAPTNet: LiDAR-Aided Perspective Transform Network
Semantic grids are a useful representation of the environment around a robot.
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning
Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation.
Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving.
Real-time Collision Risk Estimation based on Stochastic Reachability Spaces
Estimating the risk of collision with other road users is one of the most important modules to ensure safety in autonomous driving scenarios.
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network.
Ranked #1 on
Object Localization
on KITTI Pedestrian Easy
Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features
However, there is still the challenge to obtain more characteristics from the objects detected in real-time.
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles
Ground plane estimation and ground point seg-mentation is a crucial precursor for many applications in robotics and intelligent vehicles like navigable space detection and occupancy grid generation, 3D object detection, point cloud matching for localization and registration for mapping.
Attentional PointNet for 3D-Object Detection in Point Clouds
In this study, we propose Attentional Point- Net, which is a novel end-to-end trainable deep architecture for object detection in point clouds.
