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LiDAR Perception for Autonomous Driving

This training covers classical point cloud processing methods for ADAS as well as deep learning based methods for Autonomous Driving.
Code: AUT-028
Duration: 8 hours

Description

With the introduction of LiDAR sensors (Light Detection and Ranging) to ADAS (Advanced Driver Assistance Systems) and Autonomous Driving, a need for perception algorithms that directly analyze point clouds has become apparent.

In this training you’ll:
  • get an overview of ADAS and Autonomous Driving from the perspective of processing LiDAR data
  • understand the traditional system setup and coordinate frames, notions of latency and jitter
  • understand the details of classical point cloud processing algorithms for ADAS scenario
  • get hands-on experience with implementing at least one of the classical algorithms in C++
  • get an overview of deep learning approaches to perception in the autonomous driving scenario
  • understand how to measure accuracy of the algorithms and deploy them to state of the art hardware
  • get an overview of open datasets for autonomous driving

Roadmap

Brief introduction to ADAS and Autonomous Driving
  • Levels of autonomy, classic AD stack
  • Players on the market, LiDAR mount options
  • LiDAR technological directions
  • Overview of LiDAR vendors and models
  • Characteristics of Velodyne’s LiDARs
  • ASIL levels, ISO26262

Basic system setup
  • Coordinate systems (global, local, ego-vehicle, sensor, other traffic participants’)
  • Calibration
  • Synchronization
  • Latency and jitter

Classical point-cloud perception algorithms
  • Overview of perception tasks solvable with LiDAR
  • Multi-frame accumulation (motion compensation)
  • Ground detection/subtraction
  • Occupancy grid
  • Clusterization (DBscan)
  • Convex hull estimation
  • Lane detection from a point cloud

Practical exercise
  • Review of the code that implements ground plane removal, clusterization, convex hull extraction and visualization in C++ with Eigen and PCL libraries.
  • Practical task to implement one of the following algorithms: Ground plane removal with RANSAC & Convex hull calculation with Graham scan

Perception with neural networks
  • Introduction into deep learning based approaches
  • Taxonomy of neural networks for point cloud processing
  • Basic block: PointNet
  • VoxelNet (BEV detection)
  • SECOND (BEV detection)
  • PointPillars (BEV detection)
  • Fast and Furious (BEV detection and prediction)
  • Frustum PointNet (projection view, detection)
  • MV3D (mutiview detection)
  • Multiview fusion, MVF (mutiview detection)
  • Multi-View LidarNet (multitarget: segmentation and detection)

Open datasets for autonomous driving
  • KITTI
  • Semantic KITTI
  • nuScenes
  • Waymo
  • Argoverse
  • Lyft Level-5
  • Udacity

Continuous deployment of deep learning models
  • Accuracy metrics
  • Non-regressive deployment

Compute platforms for autonomous driving
  • Overview of platforms: DrivePX2, Pegasus, Mobileye, Tesla’s board computer
  • TensorRT inference library

Objectives

  • After this training you’ll be able to understand a spectrum of classical and deep learning perception algorithms that process point cloud data from a LiDAR.
  • Get hands-on experience in implementing a selected algorithm in C++

Target Audience

  • This course is designed for computer vision algorithm developers in the automotive field


Schedule in Online Prices
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Online €400
Bucharest €400
Krakow €400
Wroclaw €400

Invoices for the course will be issued in local currency. All fees above can change according to training location and delivery mode and are subject to change while scheduling. Price does not include VAT.

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