Self-Driving Cars: A Survey

By Claudine Santos Badue, Rânik Guidolini, Raphael V. Carneiro, Pedro Azevedo, Vinicius B. Cardoso, Avelino Forechi, Luan F. R. Jesus, Rodrigo Berriel, Thiago Meireles Paixão, Filipe Wall Mutz and 2 more. 2019

1,226 citations recorded by the source corpus.

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “[20] X X X X X [21] X X X [22] X X [23] X [24] X X X X [25] X X X [26] X [27] X X X X X [28] X X X X Ours X X X X X X X X X”
Adaptive Conformal Inference Under Distribution Shift (2021, neutral): “For instance, deep neural networks are currently being used in self-driving cars to detect nearby objects [2] and parole decisions are being made with the assistance of complex models that combine over a hundred features [1].”
PolyLaneNet: Lane Estimation via Deep Polynomial Regression (2020, background): “Autonomous driving [1] is a challenging ﬁeld of research that has received a lot of attention in recent years.”
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection (2020, background): “Deep learning has been essential for recent advances in numerous areas, especially in autonomous driving [2].”
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (2019, background): “MOT is an essential component for many real-time applications such as autonomous driving [1]–[4] and assistive robotics [5], [6].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, methodology): “MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation [257] Perception, Planning, Control, and Coordination for Autonomous Vehicles [258] Survey of neural networks in autonomous driving [259] Self-Driving Cars: A Survey [”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “All the surveys are categorized into several sub-sections, including the overall [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], localization [14], [15], [16], [17], [18], [19],”