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Convolutional Social Pooling for Vehicle Trajectory Prediction
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Preserved Fulltext
Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. ...
In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. ...
We would like to thank Ishan Gupta and the anonymous reviewers for their useful inputs. We also gratefully acknowledge the continued support of our industry sponsors. ...
doi:10.1109/cvprw.2018.00196
dblp:conf/cvpr/DeoT18
fatcat:brxbmbe6bvee7ace34yp3gxeeu
Convolutional Social Pooling for Vehicle Trajectory Prediction
[article]
2018
arXiv
pre-print
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Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. ...
In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. ...
We would like to thank Ishan Gupta and the anonymous reviewers for their useful inputs. We also gratefully acknowledge the continued support of our industry sponsors. ...
arXiv:1805.06771v1
fatcat:pubwjt2fgfbqrpdry6u3vrekq4
An Ensemble Learning Framework for Vehicle Trajectory Prediction in Interactive Scenarios
[article]
2022
arXiv
pre-print
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Firstly, each base learner in IETP observes historical trajectories of vehicles in the scene. Then each base learner handles interactions between vehicles to predict trajectories. ...
Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. ...
IETP assembles interaction-aware models which consider interactions between vehicles as base learners to build an ensemble learner, predicting trajectories in interactive scenarios. ...
arXiv:2202.10617v2
fatcat:fwfghirnofbehc3syloox3pm6e
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
[article]
2020
arXiv
pre-print
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Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous ...
It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. ...
Since vehicle behaviors are often inter-related, especially in dense traffic, it is crucial to consider interaction-aware trajectory prediction for autonomous driving, namely, in a multi-agent setting. ...
arXiv:2003.11476v1
fatcat:vbehynx42zcltcefoy4xdwe62u
Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1903.00848v1 fatcat:gsjzu5ihbjdrrnphrap3wmsynm
Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. ...
We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden ...
This is from the seminal work [9] on interaction-aware trajectory prediction, and we change the output to the likelihood of behavior classes. • Convolutional social pooling LSTM (CLSTM). ...
arXiv:1903.00848v2
fatcat:ownit44im5ek7euz4y5x3f3lru
A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction
2021
ISPRS International Journal of Geo-Information
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Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. ...
However, traditional models consider the trajectory prediction as a simple sequence prediction task. ...
The authors used social pooling [5] for vehicle trajectory prediction and considered the impact of surrounding vehicles. ...
doi:10.3390/ijgi10050336
fatcat:k225xe24rjbindlsocugw47fy4
Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles
2022
Electronics
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Trajectory prediction of surrounding vehicles is a critical task for connected and autonomous vehicles (CAVs), helping them to realize potential dangers in the traffic environment and make the most appropriate ...
The model learns the interactions between vehicles and corrects their trajectories during the prediction process. The output is a multimodal distribution of predicted trajectories. ...
Most physics-based and maneuver-based methods do not account for interactions between vehicles, which leaves a large gap between model predictions and actual trajectories. ...
doi:10.3390/electronics11142149
fatcat:4mpm7mewkzavtmo4rjw57ncuda
A Multi-Modal States based Vehicle Descriptor and Dilated Convolutional Social Pooling for Vehicle Trajectory Prediction
[article]
2020
arXiv
pre-print
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Precise trajectory prediction of surrounding vehicles is critical for decision-making of autonomous vehicles and learning-based approaches are well recognized for the robustness. ...
Thirdly, the LSTM decoder is used to predict the probability distribution of future trajectories based on maneuvers. ...
Therefore, the spatial interaction between vehicles is very important for trajectory prediction. ...
arXiv:2003.03480v1
fatcat:l2ly5xmjnjhu3ijcmfhrop4sni
SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network
[article]
2020
arXiv
pre-print
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Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. ...
In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both higher prediction performance and consistent computational load regardless of the number ...
Additionally, a Kalman neural network is proposed in [7] for interaction-aware trajectory prediction. ...
arXiv:2002.12609v1
fatcat:hinyd4cjjnht3ekl4mby3tsiem
DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways
[article]
2021
arXiv
pre-print
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Vehicle trajectory prediction is an essential task for enabling many intelligent transportation systems. ...
This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction in highways. ...
If the vehicle is near an exit, it is more likely to change lanes repeatedly. ...
arXiv:2108.00505v1
fatcat:fpn2wttyunholigplbr5nzoy7u
An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planning
[article]
2022
arXiv
pre-print
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In this paper, we present such a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) model for predictive manoeuvre planning of AVs. ...
A memory neuron network is used to predict future trajectories of its surrounding vehicles. ...
∆x t ∆ŷ t ψ i k v i k w i kj f i kj β L 2 Step 2: Calculate the predicted maneuver M by passing the context aware grid G to the convolutional social pooling algorithm The vehicle will be at that predicted ...
arXiv:2205.10092v1
fatcat:6wuoit6jzrfdvbfaem52l2fi5i
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
2018
2018 IEEE Intelligent Vehicles Symposium (IV)
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2018 pre-print arXiv:1805.05499v1 fatcat:73jzv42vgzhzbnt2orlts2rv3u
In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. ...
We compare our approach with the prior art for vehicle motion prediction on the publicly available NGSIM US-101 and I-80 datasets. ...
CONCLUSIONS A novel LSTM based interaction aware model for vehicle motion prediction was presented in this paper, capable of making multi-modal trajectory predictions based on maneuver classes. ...
doi:10.1109/ivs.2018.8500493
dblp:conf/ivs/DeoT18
fatcat:i5biixdw7faxvpqtin43filnim
Interaction-aware Kalman Neural Networks for Trajectory Prediction
[article]
2020
arXiv
pre-print
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as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter network ...
Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for traffic trajectory prediction. ...
., Ltd.) and Ruihui Zhao (Tencent Jarvis Lab) for their useful suggestions and contributions. ...
arXiv:1902.10928v3
fatcat:ibz3zk67pjhj5h3mkc3o6kgaue
Non-local Social Pooling for Vehicle Trajectory Prediction
2019
2019 IEEE Intelligent Vehicles Symposium (IV)
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In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. ...
For an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. ...
Non-local Social Pooling for Vehicle Trajectory Prediction Kaouther Messaoud 1 , Itheri Yahiaoui 2 , Anne Verroust-Blondet 1 and Fawzi Nashashibi 1 Abstract-For an efficient integration of autonomous vehicles ...
doi:10.1109/ivs.2019.8813829
dblp:conf/ivs/MessaoudYVN19
fatcat:byh6fftbxzbpfpt33kg6sjybnq
Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers
2021
Sensors
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Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles. ...
Therefore, to predict the trajectories of surrounding vehicles, interactions among multiple maneuvers should be considered. ...
For example, Li et al. [6] proposed a graph-based interaction-aware trajectory prediction model (GRIP). ...
doi:10.3390/s21165354
pmid:34450796
pmcid:PMC8400098
fatcat:s7hjf3o25vfb7op2fai7hokkxe
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