AIMC Topic: Automobiles

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Lightweight deep neural networks: Optimization of vehicle classification using ICBAM based on depthwise separable convolutions.

PloS one
Vehicle classification is a core task in intelligent transportation systems, where high demands are placed on both computational efficiency and generalization ability in practical applications. Existing deep learning models often struggle to meet the...

Non-destructive defect detection in powder metallurgy automotive oil pump stators using acoustic signals and machine learning classification.

PloS one
Defects such as cracks and mass reduction frequently occur during the production of powder metallurgy (PM) automotive oil pump stators, making rigorous inspection essential for reliable operation. Conventional human visual inspection is threshold-bas...

Automatic road damage recognition based on improved YOLOv11 with multi-scale feature extraction and fusion attention mechanism.

PloS one
Rapid urbanization and growing traffic volumes have increased the demand for efficient and accurate road damage detection to ensure traffic safety and optimize maintenance. Traditional manual and vehicle-mounted inspection methods are often inefficie...

An interpretable stacking ensemble learning model for visual-manual distraction level classification for in-vehicle interactions.

Accident; analysis and prevention
Recognizing the level of driver distraction during the execution of secondary tasks within the intelligent cockpit is crucial for ensuring a seamless interaction between human drivers and intelligent vehicle systems. To address this issue, this paper...

Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Deep generative models (DGMs) have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restor...

Effect of riding experience and HMI on users' trust and riding comfort in fully driverless autonomous vehicles: An on-road study.

Applied ergonomics
The wide adoption of autonomous vehicles (AVs) or robot taxis relies on technological advancements and public acceptance, which can be influenced by users' trust in AVs and comfort during rides. Among the influential factors of riding comfort, motion...

Pattern recognition in crash clusters involving vehicles with advanced driving technologies.

Accident; analysis and prevention
Autonomous Vehicle (AV) technologies, including Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), have significant potential to reduce crashes caused by driver errors. However, as AVs become more prevalent on roadways, th...

Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments.

PloS one
Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an ex...

Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm.

PloS one
Braking energy recovery is crucial for improving the energy efficiency and extending the range of electric vehicles. If a large amount of braking energy is wasted, it will lead to problems such as reduced range and increased battery burden for electr...

Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture.

Accident; analysis and prevention
The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is generating critical testing scenarios to accurately evaluate vehicle performance. To overcome the limita...