TY - GEN
T1 - Deep predictive models for collision risk assessment in autonomous driving
AU - Strickland, Mark
AU - Fainekos, Georgios
AU - Ben Amor, Hani
N1 - Funding Information:
This work was supported in part by the NSF I/UCRC Center for Embedded Systems (CES) and from NSF grants 1361926 and 1446730. The authors would also like to thank Bosch and Toyota for their support and feedback through CES.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.
AB - 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. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.
UR - http://www.scopus.com/inward/record.url?scp=85059237317&partnerID=8YFLogxK
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U2 - 10.1109/ICRA.2018.8461160
DO - 10.1109/ICRA.2018.8461160
M3 - Conference contribution
AN - SCOPUS:85059237317
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4685
EP - 4692
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Y2 - 21 May 2018 through 25 May 2018
ER -