Safe Driving at Traffic Lights: An Image Recognition Based Approach

With the increasing number of vehicles, the number of traffic accidents also increases, especially at traffic lights. To enhance the driving safety at traffic lights, in this paper, we propose an intelligent safe driving assistant to provide drivers with driving advice based on traffic light phases, which information has been neglected by existing research. The driving assistant consists of an image recognition system with a single on-board camera, which can ameliorate the difficulties of observing traffic light phases. The recognition system obtains traffic light countdown information using a Convolutional Neural Network, and estimates the countdown time using the results of traffic light information. In addition, we develop a model to calculate the distance between the traffic light and vehicle by using the information of camera and traffic light. Based on the traffic light phase and the distance obtained, the driving assistant can provide a velocity control strategy to improve driver's safety. Finally, extensive experiments are conducted to verify the effectiveness of proposed methods.

[1]  Slobodan Ilic,et al.  Semantic Segmentation Based Traffic Light Detection at Day and at Night , 2015, GCPR.

[2]  Matthieu Molinier,et al.  Traffic monitoring and modeling for Intersection Safety , 2010, Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing.

[3]  William R. Black,et al.  Sustainable Transportation: Problems and Solutions , 2010 .

[4]  Pichai Taneerananon,et al.  Remedying Traffic Accidents at Intersections Through the Use of ITS Technology , 2012 .

[5]  D. Shinar.  Traffic Safety and Human Behavior , 2007 .

[6]  Toshiaki Kondo,et al.  Red traffic light detection using fast radial symmetry transform , 2014, 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[7]  Ju H. Park,et al.  Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features , 2013 .

[8]  Kanok Boriboonsomsin,et al.  Dynamic Eco-Driving for Signalized Arterial Corridors and Its Indirect Network-Wide Energy/Emissions Benefits , 2013, J. Intell. Transp. Syst..

[9]  Evangelos Dermatas,et al.  Traffic Lights Detection in Adverse Conditions using Color, Symmetry and Spatiotemporal Information , 2012, VISAPP.

[10]  Jinheoun Choi,et al.  Proactive vehicle emissions quantification from crash potential under stop-and-go traffic conditions , 2016 .

[11]  Fawzi Nashashibi,et al.  Traffic light recognition using image processing compared to learning processes , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Pietro Cerri,et al.  Robust real-time traffic light detection and distance estimation using a single camera , 2015, Expert Syst. Appl..

[13]  Yi Li,et al.  Real-time recognition system of traffic light in urban environment , 2012, 2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications.