One of the major challenges of autonomous vehicles (AV) is their interaction with pedestrians. Unofficial interactions such as gestures, eye contact, waving, and flashing lights are very common behavioral patterns for drivers to express their intent to give priority. In our research we composed a virtual reality experiment for a pedestrian crossing in an urban environment in order to test pedestrians' reactions on an LED light display mounted on a virtual AV. Our main research interest was to investigate whether communication patterns influence the decision making of pedestrians when crossing the road. In a VR environment, four scenarios were created with a vehicle approaching a pedestrian crossing with different speeds and displaying a special red/green sign to pedestrians. Here, 51 persons participating in the experiment had to decide when crossing is safe. Results show that the majority of people indicated they would cross in the time windows when it was actually safe to cross. Male subjects made their decision to cross slightly faster but no significant differences were found in the decision making by gender. It was found that age is not an influencing factor, either. Overall, a quick learning process was experienced proving that explicit communication patterns are self-explaining.
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http://dx.doi.org/10.3390/s23031049 | DOI Listing |
PLoS One
January 2025
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, China.
Highway guardrails are critical safety infrastructure along roadways, designed to redirect vehicles back into their lanes and facilitate a gradual deceleration to a complete stop. Traditional highway steel guardrails exhibit significant limitations, including inadequate energy absorption, susceptibility to corrosion, and an increased risk of vehicles leaving the roadway during severe collisions. Furthermore, the production and transportation of these guardrails contribute to substantial carbon emissions and environmental pollution.
View Article and Find Full Text PDFJ 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.
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