Advanced driver assistance systems are required to detect latent hazards posed by surrounding vehicles and generate an appropriate response to enhance safety. Lane changes constitute potentially risky maneuvers, as drivers involved encounter latent hazards due to surrounding vehicles. A careful study of lane change behavior is therefore essential in identifying potential abnormalities that may lead to various hazards, during the process of a lane change. In this study, an anomaly detection technique is used to compare snapshots of normal and dangerous lane change maneuvers, to identify the abnormal instances. A one-class support vector machine is used and tested for novelty identification of naturalistic driving study data. The results show that the technique is able to detect dangerous lane changes with high accuracy. In addition, results suggest that dangerous behavior could occur before, after or during a lane change maneuver.

This paper proposes a novel data-driven technique, namely Online Learning for sensor Fault diagnosis Analysis (OLFA), to perform real-time fault analysis for autonomous cars. Considering the non-stationary properties of real-time sensor faults and the mapping relationship between sensors and feature variables, the proposed method decomposes the sensor fault diagnosis analysis problem into an online data stream classification and feature ranking problems. To detect and identify faults, a clustering-based data stream classification approach is developed to continuously capture and classify non-stationary sensor faults for autonomous cars with little intervention from human experts. An effective active learning method is extended and embedded into the proposed framework to minimize the need for prior knowledge about faults and enable the continual learning capability to adapt to and handle the non-stationary properties of sensor faults. Moreover, the proposed framework addresses the parameter optimization issue of existing machine learning based fault analysis techniques and employs feature ranking analysis to systematically analyze the possible source(s) of sensor faults. CAR Learning to Act (CARLA), a well-known realistic autonomous driving simulator, is used as the benchmark to perform the sensor fault injection and online data stream collection to evaluate the efficacy of OLFA. Analysis of the collected faulty datasets and experimental results, and comparison between OLFA and several state-of-the-art clustering-based approaches for fault classification, demonstrated the efficacy of the proposed framework in the domain of autonomous cars.

The integration of connected and automated vehicles (CAVs) into rural areas offers a promising solution to the unique challenges posed by limited infrastructure. This paper provides an in-depth review of the key data communication challenges that impede the seamless deployment of CAVs in these environments. We examine the development of vehicular communication technologies, evaluating their performance and the limitations of current systems in rural settings. The study explores advanced network architectures, protocols, and technologies such as vehicle-to-everything (V2X) communications, with a focus on enhancing network coverage, improving data transmission reliability, and ensuring robust security. Additionally, we analyze the specific characteristics of rural transportation networks, highlighting the need for adaptable technologies and improved infrastructure to support CAV deployment. The paper also discusses the integration of intelligent transportation systems (ITS) with CAVs, emphasizing their potential to improve safety, efficiency, and quality of life in rural communities. Through detailed text mining and topic modeling analysis, we identify current trends, challenges, and research gaps in communication networks for CAVs in rural areas, offering a comprehensive understanding of the landscape and guiding future research efforts. Our findings stress the importance of enhancing network connectivity, developing resilient communication systems, and addressing cybersecurity concerns to realize the benefits of CAVs in rural areas fully. This work contributes to the existing body of knowledge by proposing innovative solutions to overcome the infrastructural, technological, and policy-related barriers that currently prevent the effective deployment of CAVs in rural transportation systems.