A lane change detection approach using feature ranking with maximized predictive power, 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp.108-114, 2014. ,
DOI : 10.1109/ivs.2014.6856491
Vehicle trajectory prediction based on motion model and maneuver recognition, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4363-4369, 2013. ,
DOI : 10.1109/iros.2013.6696982
URL : https://hal.archives-ouvertes.fr/hal-00881100
How would surround vehicles move? a unified framework for maneuver classification and motion prediction, IEEE Transactions on Intelligent Vehicles, vol.3, issue.2, pp.129-140, 2018. ,
Surround vehicles trajectory analysis with recurrent neural networks, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp.2267-2272, 2016. ,
Generalizable intention prediction of human drivers at intersections, 2017 IEEE Intelligent Vehicles Symposium (IV), pp.1665-1670, 2017. ,
Deep neural networks for markovian interactive scene prediction in highway scenarios, 2017 IEEE Intelligent Vehicles Symposium (IV), pp.685-692, 2017. ,
An lstm network for highway trajectory prediction, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp.353-359, 2017. ,
Intentionaware long horizon trajectory prediction of surrounding vehicles using dual lstm networks, 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp.1441-1446, 2018. ,
Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms, 2018 IEEE Intelligent Vehicles Symposium (IV), pp.1179-1184, 2018. ,
Long short term memory for driver intent prediction, 2017 IEEE Intelligent Vehicles Symposium (IV), pp.1484-1489, 2017. ,
DOI : 10.1109/ivs.2017.7995919
Social LSTM: Human trajectory prediction in crowded spaces, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.961-971, 2016. ,
DOI : 10.1109/cvpr.2016.110
URL : https://infoscience.epfl.ch/record/230265/files/CVPR16_N_LSTM.pdf
Convolutional social pooling for vehicle trajectory prediction, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), p.498, 2018. ,
DOI : 10.1109/cvprw.2018.00196
URL : http://arxiv.org/pdf/1805.06771
Attention is all you need, Neural Information Processing Systems (NIPS), 2017. ,
Us highway 101 dataset, Federal Highway Administration (FHWA), 2007. ,
Us highway i-80 dataset, Federal Highway Administration (FHWA), 2006. ,
The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems, 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp.2118-2125, 2018. ,
A survey on motion prediction and risk assessment for intelligent vehicles, ROBOMECH Journal, vol.1, issue.1, pp.1-14, 2014. ,
Probabilistic prediction from planning perspective: Problem formulation, representation simplification and evaluation metric, 2018 IEEE Intelligent Vehicles Symposium (IV), pp.1150-1156, 2018. ,
DOI : 10.1109/ivs.2018.8500697
URL : https://hal.archives-ouvertes.fr/hal-01981612
Deterministic sampling-based switching kalman filtering for vehicle tracking, 2006 IEEE Intelligent Transportation Systems Conference, pp.1340-1345, 2006. ,
DOI : 10.1109/itsc.2006.1707409
Where will the oncoming vehicle be the next second, 2008 IEEE Intelligent Vehicles Symposium, pp.1068-1073, 2008. ,
DOI : 10.1109/ivs.2008.4621210
Imm-based lanechange prediction in highways with low-cost gps/ins, IEEE Transactions on Intelligent Transportation Systems, vol.10, issue.1, pp.180-185, 2009. ,
Statistical threat assessment for general road scenes using monte carlo sampling, IEEE Transactions on Intelligent Transportation Systems, vol.9, issue.1, pp.137-147, 2008. ,
Probabilistic trajectory prediction with gaussian mixture models, 2012 IEEE Intelligent Vehicles Symposium, pp.141-146, 2012. ,
DOI : 10.1109/ivs.2012.6232277
The multilayer perceptron approach to lateral motion prediction of surrounding vehicles for autonomous vehicles, 2016 IEEE Intelligent Vehicles Symposium (IV), pp.1307-1312, 2016. ,
High-speed highway scene prediction based on driver models learned from demonstrations, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp.149-155, 2016. ,
Interaction-aware driver maneuver inference in highways using realistic driver models, 2017 IEEE International Conference on Intelligent Transportation Systems, ITSC, pp.1-8, 2017. ,
Sequence-to-sequence prediction of vehicle trajectory via lstm encoder-decoder architecture, 2018 IEEE Intelligent Vehicles Symposium (IV), pp.1672-1678, 2018. ,
Learning phrase representations using RNN encoderdecoder for statistical machine translation, CoRR, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01433235
Xception: Deep learning with depthwise separable convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1800-1807, 2017. ,
DOI : 10.1109/cvpr.2017.195
URL : http://arxiv.org/pdf/1610.02357
Non-local neural networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.7794-7803, 2018. ,
DOI : 10.1109/cvpr.2018.00813
URL : http://arxiv.org/pdf/1711.07971
Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. ,
DOI : 10.1109/cvpr.2016.90
URL : http://arxiv.org/pdf/1512.03385
Adam: A method for stochastic optimization, CoRR, 2014. ,
Automatic differentiation in pytorch, NIPS 2017 Autodiff Workshop: The Future of Gradientbased Machine Learning Software and Techniques, 2017. ,
A critical evaluation of the next generation simulation (ngsim) vehicle trajectory dataset, Transportation Research Part B: Methodological, vol.105, p.2017 ,