Object Detection in Autonomous Vehicles: Status and Open Challenges

TL;DR

This paper reviews the current state of object detection techniques crucial for autonomous vehicles, highlighting recent advances, challenges, and the importance of deep learning for real-time perception in safety-critical driving scenarios.

Contribution

It provides a comprehensive overview of state-of-the-art object detectors and discusses open challenges for their deployment in autonomous vehicle perception systems.

Findings

Deep learning-based detectors are essential for real-time object localization.

Current challenges include robustness and computational efficiency.

Object detection accuracy directly impacts autonomous driving safety.

Abstract

Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.