# Building the Future of Transportation: A Comprehensive Survey on AV Perception, Localization, and Mapping

## TL;DR

This survey reviews current methods for perception, localization, and mapping in autonomous vehicles, focusing on object detection, tracking, and environmental navigation techniques.

## Contribution

A comprehensive review and evaluation of state-of-the-art perception, localization, and mapping techniques for autonomous vehicles.

## Key findings

- YOLOv8 and tracking algorithms like ByteTrack and BoT-SORT are effective for real-time object detection and tracking in urban environments.
- Sensor fusion and LiDAR-based localization improve positional accuracy in GPS-denied environments.
- SLAM and HD maps are critical for creating detailed environmental representations for autonomous navigation.

## Abstract

Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing methodologies and best practices in these domains, focusing on object detection, object tracking, localization techniques, and environmental mapping strategies. In the perception module, we analyze state-of-the-art object detection frameworks, such as You Only Look Once version 8 (YOLOv8), and object tracking algorithms like ByteTrack and BoT-SORT (Boosted SORT). We assess their real-time performance, robustness to occlusions, and suitability for complex urban environments. We examine different approaches for localization, including Light Detection and Ranging (LiDAR)-based localization, camera-based localization, and sensor fusion techniques. These methods enhance positional accuracy, particularly in scenarios where Global Positioning System (GPS) signals are unreliable or unavailable. The mapping section explores Simultaneous Localization and Mapping (SLAM) techniques and high-definition (HD) maps, discussing their role in creating detailed, real-time environmental representations that enable autonomous navigation. Additionally, we present insights from our testing, evaluating the effectiveness of different perception, localization, and mapping methods in real-world conditions. By summarizing key advancements, challenges, and practical considerations, this paper provides a reference for researchers and developers working on autonomous vehicle perception, localization, and mapping.