Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies

TL;DR

This survey reviews the integration of deep learning techniques in autonomous driving, covering perception, mapping, prediction, and safety, highlighting recent advances and key challenges in the field.

Contribution

It provides a comprehensive overview of deep learning applications in autonomous driving, focusing on perception, sensor fusion, and trajectory prediction, and discusses recent progress and future directions.

Findings

Deep learning has significantly advanced perception and sensor fusion in autonomous driving.

Deep models improve object detection, depth estimation, and trajectory prediction accuracy.

Survey highlights key challenges and future research directions in deep learning for autonomous vehicles.

Abstract

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. This is a survey of autonomous driving technologies with deep learning methods. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task level respectively, behavior modelling and prediction of vehicle driving and pedestrian trajectories.