Search Results for author:
Found 7 papers, 0 papers with code
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection.
Near Field iToF LIDAR Depth Improvement from Limited Number of Shots
Indirect Time of Flight LiDARs can indirectly calculate the scene's depth from the phase shift angle between transmitted and received laser signals with amplitudes modulated at a predefined frequency.
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency.
A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors
In this paper, a deep learning approach is presented for direction of arrival estimation using automotive-grade ultrasonic sensors which are used for driving assistance systems such as automatic parking.
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space.
BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving
Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency.
Exploring Deep Spiking Neural Networks for Automated Driving Applications
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc.
