A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

Abstract

For an autonomous vehicle to operate safely and effectively, an accurate and robust localisation system is essential. While there are a variety of vehicle localisation techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localisation techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g. cornering, intersections) and different environmental conditions (e.g. darkness, snow). The paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localisation systems with high accuracy and robustness however their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

“…Localization is the task of finding ego-position relative to a reference frame in an environment [106], and it is fundamental to any mobile robot. It is especially crucial for ADSs [25]; the vehicle must use the correct lane and position [97], [105] to localize our vehicle in the Nagoya University campus.…”

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

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Deep-Learning-Based Pedestrian Inertial Navigation: Methods, Data Set, and On-Device Inference

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