A LiDAR-assisted Smart Car-following Framework for Autonomous Vehicles

Yi, Xianyong; Ghazzai, Hakim; Massoud, Yehia

doi:10.1109/iscas46773.2023.10181437

Cited by 2 publications

(1 citation statement)

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“…Then, the receiving device detects these reflected signals and computes the distance by measuring the time it takes for the signal to travel. This calculation is performed for several points which creates a 3D representation of the vehicle environment's geometry called point cloud [45]. LiDAR sensors can be classified into two main types: mechanical LiDAR and Solid-State LiDAR (SSL) (see [40] for more details).…”

Section: ) Main Sensorsmentioning

confidence: 99%

Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges

Baccari,

Hadded,

Ghazzai

et al. 2024

IEEE Access

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In Intelligent Transportation Systems (ITS), ensuring road safety has paved the way for innovative advancements such as autonomous driving. These self-driving vehicles, with their variety of sensors, harness the potential to minimize human driving errors and enhance transportation efficiency via sophisticated AI modules. However, the reliability of these sensors remains challenging, especially as they can be vulnerable to anomalies resulting from adverse weather, technical issues, and cyber-attacks. Such inconsistencies can lead to imprecise or erroneous navigation decisions for autonomous vehicles that can result in fatal consequences, e.g., failure in recognizing obstacles. This survey delivers a comprehensive review of the latest research on solutions for detecting anomalies in sensor data. After laying the foundation on the workings of the connected and autonomous vehicles, we categorize anomaly detection methods into three groups: statistical, classical machine learning, and deep learning techniques. We provide a qualitative assessment of these methods to underline existing research limitations. We conclude by spotlighting key research questions to enhance the dependability of autonomous driving in forthcoming studies.

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Section: ) Main Sensorsmentioning

confidence: 99%