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End-to-end Autonomous Driving

End-to-End Autonomous System
Transfer from Simulation to Real(Sim2Real)
RL-based End-to-end Autonomous Driving
Architecture of RL-SESR(Segmented Encoder for Sim2Real)
RL-SESR Field Test
IL-based End-to-end Autonomous Driving
Architecture of Imitation Learning-based E2E Autonomous Driving
IL-based E2E Driving Field Test

Sensor perception technology

Lidar point cloud lane perception dataset construction, AI-based lane and object perception
K-Lane Dataset Examples
Labeling program
Lidar lane detection inference
Lidar lane detection framework
4D Radar object perception technology
4D Radar object detection inference result
Sensor measurement in heavy snow condition
4D Radar labeling process
4D Radar object detection framework
PointCloud feature extraction technology
  • Multi-resolution feature : Minimize the loss of detailed information in the downsampling process.
  • Learnable Pooling : Minimizes the loss of point feature information other than the maximum value during feature pooling.
Architecture of PointStack. As a general feature learning backbone, PointStack can be used for various tasks such as classification and segmentation
Part-segmentation visualization of ground truths (G.T.) and predictions (Pred)
LPI RADAR signal detection technology
AVE lab’s LPI waveform recognition system
Performance Comparison

Advanced Positioning technology

AI-based Multipath mitigation
Input Image for in-phase
NLOS Satellite classification
Next-generation GNSS signal modulation
Short Time Fourier Transform of VBOC(6:-1:1,1)
PSDs of VBOC and conventional GNSS modulations
ACF envelopes of VBOC and conventional GNSS modulations
Cellular fingerprint