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

Abstract

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.

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

“…Improving highway safety is one of the most developed areas of ML application in ITSs. It includes security analysis of the road network and its elements [56][57][58], improving information security in ITSs [59,60], and detecting anomalies in sensor data of connected and autonomous vehicles [61][62][63]. It also involves intelligent analysis of road traffic accidents to identify critical factors in their occurrence, essential for preventing similar incidents in the future [64].…”