Key Ingredients of Self-Driving Cars

Abstract

Over the past decade, many research articles have been published in the area of autonomous driving. However, most of them focus only on a specific technological area, such as visual environment perception, vehicle control, etc. Furthermore, due to fast advances in the self-driving car technology, such articles become obsolete very fast. In this paper, we give a brief but comprehensive overview on key ingredients of autonomous cars (ACs), including driving automation levels, AC sensors, AC software, open source datasets, industry leaders, AC applications and existing challenges.

arXiv comment5 pages, 2 figures, EUSIPCO 2019 Satellite Workshop: Signal Processing, Computer Vision and Deep Learning for Autonomous Systems

References in corpus (4)

• Social Force Model for Pedestrian Dynamics
• End to End Learning for Self-Driving Cars
• Tightly Coupled 3D Lidar Inertial Odometry and Mapping
• Road surface 3d reconstruction based on dense subpixel disparity map estimation

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• Real-Time, Environmentally-Robust 3D LiDAR Localization
• Autonomous UAV Landing System Based on Visual Navigation
• Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT
• Automatic Calibration of Dual-LiDARs Using Two Poles Stickered with Retro-Reflective Tape