SafeDrive-Fi: A Multimodal and Device Free Dangerous Driving Recognition System Using WiFi

We present the first WiFi based driver state recognition system: SafeDrive-Fi. Our proposed framework extracts fine-grain Channel State Information (CSI) of WiFi signal to accurately predict driver states through gestures and body movements. Different from vision based techniques, SafeDrive-Fi provides a simple, cost-effective and ubiquitous solution to prevent accidents and loss of lives due to reckless driving. We incorporate a unique DETECT algorithm to differentiate between normal and dangerous driving in a challenging and noisy in-vehicle conditions. Using only commercially available products, SafeDrive-Fi is compatible with 802.11n/ac and can assist drivers and law enforcement in discovering dangerous driving states. To the best of our knowledge, this is the first system that aggregates information from all the channel subcarriers and use multidomain CSI features to classify dangerous driving conditions. SafeDrive-Fi achieves an overall 98.04% recognition accuracy and 19.8% improvement over similar Received Signal Strength (RSS) based solution using an already deployed infrastructure.

[1]  Fadel Adib,et al.  Emotion recognition using wireless signals , 2016, MobiCom.

[2]  Yonghe Liu,et al.  MAIS: Multiple Activity Identification System Using Channel State Information of WiFi Signals , 2017, WASA.

[3]  Daniel McDuff,et al.  AutoEmotive: bringing empathy to the driving experience to manage stress , 2014, DIS Companion '14.

[4]  Margaret M. Peden,et al.  World Report on Road Traffic Injury Prevention , 2004 .

[5]  Sang Hyuk Son,et al.  WiTraffic: Low-Cost and Non-Intrusive Traffic Monitoring System Using WiFi , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[6]  Masashi Sugiyama,et al.  Sequential change‐point detection based on direct density‐ratio estimation , 2012, Stat. Anal. Data Min..

[7]  Yongdae Kim,et al.  A machine learning framework for network anomaly detection using SVM and GA , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.

[8]  Jiming Chen,et al.  Gradient-Based Fingerprinting for Indoor Localization and Tracking , 2016, IEEE Transactions on Industrial Electronics.

[9]  Khaled A. Harras,et al.  WiGest demo: A ubiquitous WiFi-based gesture recognition system , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  Heng Li,et al.  Wi-chase: A WiFi based human activity recognition system for sensorless environments , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[11]  Yu Hu,et al.  Body-Earth Mover’s Distance: A Matching-Based Approach for Sleep Posture Recognition , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[12]  Khaled A. Harras,et al.  Wigest: A Ubiquitous Wifi-based Gesture Recognition System , 2014 .

[13]  Mohan M. Trivedi,et al.  Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[14]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[15]  Sheng Tan,et al.  WiFinger: leveraging commodity WiFi for fine-grained finger gesture recognition , 2016, MobiHoc.

[16]  Cecilia Mascolo,et al.  A Study of Bluetooth Low Energy performance for human proximity detection in the workplace , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[17]  Stephan Sigg,et al.  RFexpress! - RF emotion recognition in the wild , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[18]  Ruiyun Yu,et al.  Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network , 2018, 2018 IEEE International Conference on Communications (ICC).

[19]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[20]  Lionel M. Ni,et al.  A Survey on Wireless Indoor Localization from the Device Perspective , 2016, ACM Comput. Surv..