# Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods

## TL;DR

This paper presents a real-time LiDAR-based object detection and classification system for self-driving cars, using neural networks to identify pedestrians, vehicles, and bikers from 3D point cloud data.

## Contribution

It introduces a novel real-time detection and classification approach utilizing machine learning on LiDAR data for autonomous vehicle perception.

## Key findings

- Effective classification of pedestrians, vehicles, and bikers
- Real-time detection performance demonstrated
- Applicable to self-driving vehicle systems

## Abstract

The goal of this paper is to classify objects mapped by LiDAR sensor into different classes such as vehicles, pedestrians and bikers. Utilizing a LiDAR-based object detector and Neural Networks-based classifier, a novel real-time object detection is presented essentially with respect to aid self-driving vehicles in recognizing and classifying other objects encountered in the course of driving and proceed accordingly. We discuss our work using machine learning methods to tackle a common high-level problem found in machine learning applications for self-driving cars: the classification of pointcloud data obtained from a 3D LiDAR sensor.