Noah T. Curran (University of Michigan), Kang G. Shin (University of Michigan), William Hass (Lear Corporation), Lars Wolleschensky (Lear Corporation), Rekha Singoria (Lear Corporation), Isaac Snellgrove (Lear Corporation), Ran Tao (Lear Corporation)

ETAS Best Short Paper Award Runner-Up!

On urban roadways, “dooring” remains a serious problem to the safety of pedestrians, cyclists, and other vulnerable road users (VRUs). Existing solutions that address this concern remain inadequate, as they either place unreasonable expectations on the pedestrians or rely on prohibitively expensive additions to the vehicle’s sensing capabilities. Consequently, typical consumer vehicles are not yet equipped with such a technology, and practical dooring prevention still remains a safety concern.
To address this problem, we propose a driver safety system for dooring prevention called S-Door that uses existing resources available in every modern vehicle: Bluetooth Low-Energy (BLE). Since a modern vehicle is distributively equipped with multiple BLE transceivers, we leverage each transceiver to observe BLE advertising data (AD) packets that consumers’ smart devices passively transmit. From these AD packets, we extract information that we can use to localize the VRU device without pairing with the device. With this information, we propose two methods for localization based on BLE versions ≤5.0 and ≥5.1, respectively. Our solutions are capable of alerting the driver of all instances of an oncoming VRU. Due to S-Door’s use of existing vehicle BLE hardware, we may extend this application to modern vehicles through a firmware update—no physical modification is necessary.

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Zhenhao Luo (College of Computer, National University of Defense Technology), Pengfei Wang (College of Computer, National University of Defense Technology), Baosheng Wang (College of Computer, National University of Defense Technology), Yong Tang (College of Computer, National University of Defense Technology), Wei Xie (College of Computer, National University of Defense Technology), Xu Zhou (College of Computer,…

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OCPPStorm: A Comprehensive Fuzzing Tool for OCPP Implementations (Long)

Gaetano Coppoletta (University of Illinois Chicago), Rigel Gjomemo (Discovery Partners Institute, University of Illinois), Amanjot Kaur, Nima Valizadeh (Cardiff University), Venkat Venkatakrishnan (Discovery Partners Institute, University of Illinois), Omer Rana (Cardiff University)

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