Mobile Computing: Vehicular Technology


Early Recognition of Inattentive Driving Leveraging Audio Devices on Smartphones. Real-time driving behavior monitoring is a corner stone to improve driving safety. Most of the existing studies on driving behavior monitoring using smartphones only provide detection results after an abnormal driving behavior is finished, not sufficient for driver alert and avoiding car accidents. In this paper, we leverage existing audio devices on smartphones to realize early recognition of inattentive driving events including Fetching Forward, Picking up Drops, Turning Back and Eating or Drinking. Through empirical studies of driving traces collected in real driving environments, we propose an Early Recognition system, ER, which can recognize inattentive driving events at an early stage and alert drivers timely.

Relative Papers:
Xiangyu Xu, Hang Gao, Jiadi Yu, Yingying Chen, Yanmin Zhu, Guangtao Xue, and Minglu Li, "ER: Early Recognition of Inattentive Driving Leveraging Audio Devices on Smartphones," in Proceedings of IEEE International Conference on Computer Communications (IEEE InfoCom 2017), Atlanta, GA, USA, May 2017. [pdf][slide]
Xiangyu Xu,Jiadi Yu, Yingying Chen, Yanmin Zhu, Shiyou Qian, and Minglu Li "Leveraging Audio Signals for Early Recognition of Inattentive Driving with Smartphones," IEEE Transactions on Mobile Computing (IEEE TMC), 2018.

   

Sensing Driving Conditions for Vehicle Lane-Level Localization on Highways . When vehicle road-level localization cannot satisfy people’s need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing work on tracking vehicles on lane-level mostly depends on pre-deployed infrastructures and additional hardware. We utilize smartphone sensing of driving conditions for vehicle lane-level localization on highways and propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways.

Relative Papers:
Zhichen Wu, Jianda Li, Jiadi Yu, Yanmin Zhu, Guangtao Xue, and Minglu Li, "L3: Sensing Driving Conditions for Vehicle Lane-Level Localization on Highways," in Proceedings of the IEEE International Conference on Computer Communications (IEEE InfoCom 2016), San Francisco, USA, April 2016. [pdf] [slides]
Xiangyu Xu,Jiadi Yu, Yanmin Zhu, Zhichen Wu, Jianda Li, and Minglu Li, "Leveraging Smartphones for Vehicle Lane-Level Localization on Highways," IEEE Transactions on Mobile Computing (IEEE TMC), 2018. [pdf]

   

Abnormal Driving Behaviors Detection and Identification using Smartphone Sensors. Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained abnormal driving behaviors monitoring approach, which can not only detect abnormal driving behaviors but also identify specific types of abnormal driving behaviors, i.e. Weaving, Swerving, Sideslipping, Fast U-turn, Turning with a wide radius and Sudden braking. In this project, we propose a fine-grained abnormal Driving behavior Detection and iDentification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors.

Relative Papers:
Jiadi Yu, Zhongyang Chen, Yanmin Zhu, Yingying Chen, Linghe Kong, Minglu Li, "Fine-grained Abnormal Driving Behaviors Detection and Identification with Smartphones," IEEE Transactions on Mobile Computing (IEEE TMC), 2017. [pdf]
Zhongyang Chen, Jiadi Yu, Yanmin Zhu, Yingying Chen, Minglu Li "D3: Abnormal Driving Behaviors Detection and Identification using Smartphone Sensors," in Proceedings of the IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2015), Seattle, USA, July 2015. [pdf] [slides]

   

Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments. The smartphone-based vehicular applications become more and more popular to analyze the increasingly complex urban traffic flows and facilitate more intelligent driving experiences including vehicle localization, enhancing driving safety, driving behavior analysis and building intelligent transportation systems. Among these applications, the vehicle speed is an essential input. Accurate vehicle speed estimation could make those vehicle-speed dependent applications more reliable under complex traffic systems in urban environments. In this project, we utilize smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments.

Relative Papers:
Jiadi Yu, Hongzi Zhu, Haofu Han, Yingying Chen, Jie Yang, Yanmin Zhu, Zhongyang Chen, Guangtao Xue, and Minglu Li, "SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments," IEEE Transactions on Mobile Computing (IEEE TMC), 15(1): 202-216 (2016). [pdf]
Haofu Han, Jiadi Yu, Hongzi Zhu, Yingying Chen, Jie Yang, Guangtao Xue, Yanmin Zhu, and Minglu Li, "SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments," in Proceedings of the IEEE International Conference on Computer Communications (IEEE InfoCom 2014), Toronto, Canada, April 2014. [pdf] [slides]