Learn from IoT - Pedestrian Detection and Intention Prediction for Autonomous Driving
| العنوان: | Learn from IoT - Pedestrian Detection and Intention Prediction for Autonomous Driving |
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| المؤلفون: | Everton Luís Berz, Jonathan Fürst, Bin Cheng, Samet Aytaç, Marzieh Farahani Dolatabadi, Jos den Ouden, Gurkan Solmaz |
| المساهمون: | Control Systems Technology, Mobile Perception Systems Lab, Video Coding & Architectures |
| المصدر: | Proceedings of the 1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability -SMAS '19 Proceedings of the 1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability-SMAS 19 SMAS@MobiCom SMAS 2019-Proceedings of the 1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability, co-located with MobiCom 2019, 27-32 STARTPAGE=27;ENDPAGE=32;TITLE=SMAS 2019-Proceedings of the 1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability, co-located with MobiCom 2019 |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Internet of things, Vulnerable road user detection, business.industry, Computer science, Pedestrian detection, Deep learning, Real-time computing, Autonomous vehicles, 020206 networking & telecommunications, 02 engineering and technology, Pedestrian, University campus, deep neural networks, autonomous vehicles, deep neural networks, internet of things, vulnerable road user detection, 0202 electrical engineering, electronic engineering, information engineering, Trajectory, 020201 artificial intelligence & image processing, Artificial intelligence, Internet of Things, business, Mobile device, Road user |
| الوصف: | This paper explores the potential of machine learning (ML) systems which use data from in-vehicle sensors as well as external IoT data sources to enhance autonomous driving for efficiency and safety in urban environments. We propose a system which combines sensor data from autonomous vehicles and IoT data collected from pedestrians' mobile devices. Our approach includes two methods for vulnerable road user (VRU) detection and pedestrian movement intention prediction, and a model to combine the two outputs for potentially improving the autonomous decision-making. The first method creates a world model (WM) and accurately localizes VRUs using in-vehicle cameras and external mobile device data. The second method is a deep learning model to predict pedestrian's next movement steps using real-time trajectory and training with historical mobile device data. To test the system, we conduct three pilot tests at a university campus with a custom-built autonomous car and mobile devices carried by pedestrians. The results from our controlled experiments show that VRU detection can more accurately distinguish locations of pedestrians using IoT data. Furthermore, up to five future steps of pedestrians can be predicted within 2 m. |
| DOI: | 10.1145/3349622.3355446 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd7ea976dec2a6cc39b4f81bb1c7fdce |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....bd7ea976dec2a6cc39b4f81bb1c7fdce |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.1145/3349622.3355446 |
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