The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.
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http://dx.doi.org/10.3390/s24061977 | DOI Listing |
J Acoust Soc Am
January 2025
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Developing persistent and smart underwater markers is critical for improving navigation accuracy and communication capabilities of autonomous underwater vehicles (AUVs). A wireless acoustic identification tag, which uses a piezoelectric transducer tuned in the broadband ultrasonic range (200-500 kHz), was experimentally demonstrated to achieve highly efficient power transfer (source-to-tag electrical power efficiency of >2% at 6 m) and concurrent high data rate and backscatter level communication (>83.3 kbit s-1, >170 dB sound pressure level at 6 m) with potential operating range ≈ 10 m based on analytical extrapolations.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune 411044, India.
Recent advancements in artificial intelligence (AI) have increased interest in intelligent transportation systems, particularly autonomous vehicles. Safe navigation in traffic-heavy environments requires accurate road scene segmentation, yet traditional computer vision methods struggle with complex scenarios. This study emphasizes the role of deep learning in improving semantic segmentation using datasets like the Indian Driving Dataset (IDD), which presents unique challenges in chaotic road conditions.
View Article and Find Full Text PDFSci Rep
January 2025
School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China.
Lane detection is one of the key functions to ensure the safe driving of autonomous vehicles, and it is a challenging task. In real driving scenarios, external factors inevitably interfere with the lane detection system, such as missing lane markings, harsh weather conditions, and vehicle occlusion. To enhance the accuracy and detection speed of lane detection in complex road environments, this paper proposes an end-to-end lane detection model with a pure Transformer architecture, which exhibits excellent detection performance in complex road scenes.
View Article and Find Full Text PDFAccid Anal Prev
January 2025
Department of Civil Engineering, The University of British Columbia, Canada.
Proactive and holistic safety management approaches should consider multi-modal crash risk. Cyclist crash risk should be prioritized given the high-severity of vehicle-cyclist crashes. Cyclist crash risk is difficult to quantify given the sparse nature of cyclist collisions and collisions in general.
View Article and Find Full Text PDFHeliyon
January 2025
Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai, 200063, China.
Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data.
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