Shouyi Yin
ABSTRACT
Driving behavior is directly related to the lives and property safety of the drivers and others, good driving behavior can not only reduce the accident rate, but also reduce the driving risk. In this paper, an effective driving behavior evaluation method is proposed. Features integration is very important, we propose the multi-CNN architecture, it has a higher prediction accuracy. Convolutional neural network is time-consuming and computation intensive, a dynamic fixed point compression method is applied in our system, smaller model size and faster speed can be achieved while the accuracy is high. The lanes, cars and pedestrians on the road are detected, meanwhile, the distance between the host car and the nearest car in front of it is calculated. These data are predicted by a trained Gradient Boosting Decision Tree model, the prediction result is a driving score that can reflect the driver's driving behavior is good or bad. The root mean square error of our model is 1.9, which has a high accuracy and is useful in practice.
- X. An, M. Wu, and H. He. A novel approach to provide lane departure warning using only one forward-looking camera. In Collaborative Technologies and Systems, 2006. CTS 2006. International Symposium on, pages 356--362. IEEE, 2006. Google Scholar
Digital Library
- M. I. Arenado, J. M. P. Oria, C. Torre-Ferrero, and L. A. Rentería. Monovision-based vehicle detection, distance and relative speed measurement in urban traffic. IET Intelligent Transport Systems, 8(8):655--664, 2014. Google ScholarCross Ref
- M. Beyeler, F. Mirus, and A. Verl. Vision-based robust road lane detection in urban environments. In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages 4920--4925. IEEE, 2014. Google ScholarCross Ref
- J.-Y. Chang and C.-W. Cho. Vision-based front vehicle detection and its distance estimation. In Systems, Man and Cybernetics, 2006. SMC'06. IEEE International Conference on, volume 3, pages 2063--2068. IEEE, 2006. Google ScholarCross Ref
- J. Chen, X. Zhang, H. Wang, Q. Shi, and R. Li. Pedestrian detection combining rgb and dense lidar data. In Ieee/rsj International Conference on Intelligent Robots and Systems, pages 4112 -- 4117, 2014.Google Scholar
- X. Chen and H. Ma. 3d object proposals for accurate object class detection. 2015.Google Scholar
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886--893. IEEE, 2005. Google ScholarDigital Library
- S. E. Forward. The theory of planned behaviour: The role of descriptive norms and past behaviour in the prediction of driversâĂŹ intentions to violate. Transportation Research Part F: Traffic Psychology and Behaviour, 12(3):198--207, 2009. Google ScholarCross Ref
- J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189--1232, 2001. Google ScholarCross Ref
- V. Gaikwad and S. Lokhande. Lane departure identification for advanced driver assistance. Intelligent Transportation Systems, IEEE Transactions on, 16(2):910--918, 2015.Google Scholar
- A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In Conference on Computer Vision and Pattern Recognition (CVPR), 2012. Google ScholarDigital Library
- X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, et al. Practical lessons from predicting clicks on ads at facebook. In Proceedings of 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1--9. ACM, 2014. Google ScholarDigital Library
- M. Hörwick and K.-H. Siedersberger. Strategy and architecture of a safety concept for fully automatic and autonomous driving assistance systems. In Intelligent Vehicles Symposium (IV), 2010 IEEE, pages 955--960. IEEE, 2010. Google ScholarCross Ref
- J. Krause, M. Stark, J. Deng, and L. Fei-Fei. 3d object representations for fine-grained categorization. In Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on, pages 554--561. IEEE, 2013. Google ScholarDigital Library
- B. Li, T. Wu, and S. C. Zhu. Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. 2014.Google Scholar
- E. Ohn-Bar and M. M. Trivedi. Learning to detect vehicles by clustering appearance patterns. IEEE Transactions on Intelligent Transportation Systems, 16(5):2511--2521, 2015. Google ScholarCross Ref
- B. Pepikj, M. Stark, P. Gehler, and B. Schiele. Multi-view and 3d deformable part models. IEEE Transactions on Pattern Analysis Machine Intelligence, 37(11):2232--2245, 2015. Google ScholarDigital Library
- S. G. Philipp Gysel, Mohammad Motamedi. Hardware-oriented approximation of convolutional neural networks. arxiv, 2016.Google Scholar
- S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In Neural Information Processing Systems (NIPS), 2015. Google ScholarDigital Library
- R. K. Satzoda and M. M. Trivedi. Efficient lane and vehicle detection with integrated synergies (elvis). In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pages 708--713. IEEE, 2014. Google ScholarDigital Library
- Y. Tian, P. Luo, X. Wang, and X. Tang. Deep learning strong parts for pedestrian detection. In IEEE International Conference on Computer Vision, 2015. Google ScholarDigital Library
- P. Tseng. On accelerated proximal gradient methods for convex-concave optimization. Siam Journal on Optimization, 2008.Google Scholar
- J. Wu and C. Gu. The design and implementation of real-time automatic vehicle detection and counting system. In Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on, pages 1--4. IEEE, 2009. Google ScholarCross Ref
- Y. Xiang, W. Choi, Y. Lin, and S. Savarese. Data-driven 3d voxel patterns for object category recognition. In CVPR, 2015.Google ScholarCross Ref
- B. Yu, W. Zhang, and Y. Cai. A lane departure warning system based on machine vision. In Computational Intelligence and Industrial Application, 2008. PACIIA'08. Pacific-Asia Workshop on, volume 1, pages 197--201. IEEE, 2008. Google ScholarDigital Library
- H. Zhao, H. Zhou, C. Chen, and J. Chen. Join driving: A smart phone-based driving behavior evaluation system. In Global Communications Conference (GLOBECOM), 2013 IEEE, pages 48--53. IEEE, 2013.Google ScholarCross Ref
- Q. Zhou, G. Wang, K. Jia, and Q. Zhao. Learning to share latent tasks for action recognition, pages 2264--2271, 2013. Google ScholarDigital Library
Index Terms
- Multi-CNN and decision tree based driving behavior evaluation
Recommendations
Design of Test System on Nighttime Driving Behavior and ECG Characteristics of Long-distance Bus Drivers (I) - Test Design
ICIE '10: Proceedings of the 2010 WASE International Conference on Information Engineering - Volume 03For the safety issues of night long-distance bus drivers, a test system that real-time continuously acquires vehicles driving parameter, traffic environmental images as well as ECG physiological state of drivers under night-time driving conditions is ...
Investigating the effects of an advance warning in-vehicle system on behavior and attention in controlled driving
AutomotiveUI '11: Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsAdvance warning systems constitute a class of in-vehicle systems that could influence and improve driving in the future, e.g. by warning of accidents or slippery roads. However, the effects of advance warning systems are still poorly understood and ...
Driver Identification Using Driving Behavior Signals
In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, ...
Comments