2017

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

Strickland, Mark, Fainekos, Georgios, Amor, Heni Ben

Understand

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving.

A deep predictive model is trained to anticipate imminent accidents from traditional video streams.
In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations.
In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task.

Built on

R. Kohavi et al. , “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Ijcai , vol. 14, no. 2. Stanford, CA, 1995, pp. 1137–1145
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. Hadsell, P. Sermanet, J. Ben, A. Erkan, M. Scoffier, K. Kavukcuoglu, U. Muller, and Y. LeCun, “Learning long-range vision for autonomous off-road driving,” Journal of Field Robotics , vol. 26, no. 2, pp. 120–144, 2009
2009
Earlier work this paper cites.
B. R. Chang, H. F. Tsai, and C.-P. Young, “Intelligent data fusion system for predicting vehicle collision warning using vision/gps sensing,” Expert Systems with Applications , vol. 37, no. 3, pp. 2439–2450, 2010
2010
Earlier work this paper cites.
D. M. W. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” International Journal of Machine Learning Technology , vol. 2, no. 1, pp. 37–63, 2011
2011
Earlier work this paper cites.
C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Scene parsing with multiscale feature learning, purity trees, and optimal covers,” CoRR , vol. abs/1202.2160, 2012. [Online]. Available: http://arxiv.org/abs/1202.2160
Original
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” International Journal of Robotics Research (IJRR) , 2013
2013
Earlier work this paper cites.
A. Graves, “Generating sequences with recurrent neural networks,” CoRR , vol. abs/1308.0850, 2013. [Online]. Available: http://arxiv.org/abs/1308.0850
Original
2013
Earlier work this paper cites.
M. F. E. Rohmer, S. P. N. Singh, “V-rep: a versatile and scalable robot simulation framework,” in Proc. of The International Conference on Intelligent Robots and Systems (IROS) , 2013
2013
Earlier work this paper cites.

Similar

H. Cho, Y.-W. Seo, B. V. Kumar, and R. R. Rajkumar, “A multi-sensor fusion system for moving object detection and tracking in urban driving environments,” in Robotics and Automation (ICRA), 2014 IEEE International Conference on . IEEE, 2014, pp. 1836–1843
2014
Cited alongside, same era.
A. J. Hamlet and C. D. C. III, “Joint belief and intent prediction for collision avoidance in autonomous vehicles,” CoRR , vol. abs/1504.00060, 2015. [Online]. Available: http://arxiv.org/abs/1504.00060
Original
2015
Cited alongside, same era.
B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, F. Mujica, A. Coates, and A. Y. Ng, “An empirical evaluation of deep learning on highway driving,” CoRR , vol. abs/1504.01716, 2015. [Online]. Available: http://arxiv.org/abs/1504.01716
Original
2015
Cited alongside, same era.
A. Kendall, V. Badrinarayanan, and R. Cipolla, “Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding,” CoRR , vol. abs/1511.02680, 2015. [Online]. Available: http://arxiv.org/abs/1511.02680
Original
2015
Cited alongside, same era.
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” CoRR , vol. abs/1506.04214, 2015. [Online]. Available: http://arxiv.org/abs/1506.04214
Original
2015
Cited alongside, same era.
G. Powar, “Calculation of collision probability for autonomous vehicles using trajectory prediction,” Master’s thesis, Rutgers University, New Brunswick, 2016
2016
Cited alongside, same era.
A. Jain, H. S. Koppula, S. Soh, B. Raghavan, A. Singh, and A. Saxena, “Brain4cars: Car that knows before you do via sensory-fusion deep learning architecture,” CoRR , vol. abs/1601.00740, 2016. [Online]. Available: http://arxiv.org/abs/1601.00740
Original
2016
Cited alongside, same era.
A. Kendall and R. Cipolla, “Modelling uncertainty in deep learning for camera relocalization,” in Robotics and Automation (ICRA), 2016 IEEE International Conference on . IEEE, 2016, pp. 4762–4769
2016
Cited alongside, same era.

Then

O. Cameron. (2016) Open sourcing 223gb of driving data collected in mountain view, ca by our lincoln mkz. [Online]. Available: https://medium.com/udacity/open-sourcing-223gb-of-mountain-view-driving-data-f6b5593fbfa5
2016
Later among the works it cites.
Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Later among the works it cites.
U. of Calif. Regents. (2017) Berkeley deepdrive. [Online]. Available: http://www.path.berkeley.edu/berkeley-deepdrive
2017
Closest in time.
C. Chen, H. Xiang, T. Qiu, C. Wang, Y. Zhou, and V. Chang, “Rear-end collision prediction scheme based on deep learning in the internet of vehicles,” Journal of Parallel and Distributed Computing , 2017
2017
Closest in time.
P. Drews, G. Williams, B. Goldfain, E. A. Theodorou, and J. M. Rehg, “Aggressive deep driving: Model predictive control with a CNN cost model,” CoRR , vol. abs/1707.05303, 2017. [Online]. Available: http://arxiv.org/abs/1707.05303
Original
2017
Closest in time.
F. Lambert. (2017) Tesla has opened the floodgates of autopilot data gathering. [Online]. Available: https://electrek.co/2017/06/14/tesla-autopilot-data-floodgates/
2017
Closest in time.
M. Shobe. (2017) Sample training dataset for autonomous driving now available. [Online]. Available: https://mty.ai/blog/new-open-training-dataset-for-autonomous-driving-now-available/
2017
Closest in time.
M. Cordts. (2017) Cityscapes dataset. [Online]. Available: https://www.cityscapes-dataset.com/
2017
Closest in time.

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