Corpus ID: 199064269

DROGON: A Causal Reasoning Framework for Future Trajectory Forecast

@article{Choi2019DROGONAC,
  title={DROGON: A Causal Reasoning Framework for Future Trajectory Forecast},
  author={Chiho Choi and Abhishek Patil and Srikanth Malla},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.00024}
}
We propose DROGON (Deep RObust Goal-Oriented trajectory prediction Network) for accurate vehicle trajectory forecast by considering behavioral intention of vehicles in traffic scenes. Our main insight is that a causal relationship between intention and behavior of drivers can be reasoned from the observation of their relational interactions toward an environment. To succeed in causal reasoning, we build a conditional prediction model to forecast goal-oriented trajectories, which is trained with… Expand

Figures, Tables, and Topics from this paper

Interaction-Aware Trajectory Prediction based on a 3D Spatio-Temporal Tensor Representation using Convolutional–Recurrent Neural Networks
TLDR
This paper proposes a combination of two lines of research for predicting all the trajectories of a group of vehicles of arbitrary size, considering the mutual interactions possible, using the potential field representation as input for a neural network, which predicts a distribution over trajectories based on distinct maneuvers. Expand
Multiple Trajectory Prediction of Moving Agents with Memory Augmented Networks.
TLDR
This paper proposes MANTRA, a model that exploits memory augmented networks to effectively predict multiple trajectories of other agents, observed from an egocentric perspective, and shows how once trained the system can continuously improve by ingesting novel patterns. Expand
Multiple Future Prediction Leveraging Synthetic Trajectories
TLDR
This work proposes a data driven approach based on Markov Chains to generate synthetic trajectories, which are useful for training a multiple future trajectory predictor and shows that combining synthetic and real data leads to prediction improvements, obtaining state of the art results. Expand
Goal-driven Long-Term Trajectory Prediction
  • H. Tran, Vuong Le, T. Tran
  • Computer Science
  • 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
  • 2021
TLDR
Goal-driven Trajectory Prediction model is designed - a dual-channel neural network that realizes intuition about a hypothetical process that determines pedestrians’ goals and the impact of such process on long-term future trajectories and is shown to outperform the state-of-the-art in various settings, especially in large prediction horizons. Expand
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View With a Reachability Prior
TLDR
Experiments show that the reachability prior combined with multi-hypotheses learning improves multimodal prediction of the future location of tracked objects and, for the first time, the emergence of new objects. Expand
Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance
TLDR
A novel "Sense–Learn–Reason–Predict" framework that is able to generate collision- free trajectory predictions in a synthetic dataset designed for collision avoidance evaluation and remains competitive on the commonly used NGSIM US-101 highway dataset. Expand
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
TLDR
This paper proposes a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture that predicts interactive motions of other drivers in response to potential actions of the autonomous vehicle, which are then systematically evaluated in safety constraints. Expand
Human motion trajectory prediction: a survey
TLDR
A survey of human motion trajectory prediction and proposes a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. Expand
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
TLDR
This work presents a highfidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning in the safety-critical context and introduces agency, such that it is simple to define and create complex scenarios using high-level definitions. Expand
Cooperation-Aware Lane Change Control in Dense Traffic
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavyExpand
...
1
2
3
...

References

SHOWING 1-10 OF 33 REFERENCES
Looking to Relations for Future Trajectory Forecast
  • Chiho Choi, B. Dariush
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2019
TLDR
A relation-aware framework for future trajectory forecast that constructs pair-wise relations from spatio-temporal interactions and identifies more descriptive relations that highly influence future motion of the target road user by considering its past trajectory is proposed. Expand
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
TLDR
A variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors that improves the prediction error of a physics-based model by 25% while successfully identifying the uncertain cases with 82% accuracy is proposed. Expand
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
TLDR
The proposed long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict, uses an instance layer to learn instances' movements and interactions and has a category layer to learning the similarities of instances belonging to the same type to refine the prediction. Expand
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
TLDR
A novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle using a multi-stream recurrent neural network encoder-decoder model that separately captures both object location and Scale and pixel-level observations for future vehicle localization is introduced. Expand
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
TLDR
This paper presents an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways and assigns confidence values to maneuvers being performed by vehicles and outputs a multi-modal distribution over future motion based on them. Expand
Social LSTM: Human Trajectory Prediction in Crowded Spaces
TLDR
This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets. Expand
Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model
TLDR
A new spatio-temporal graph based Long Short-Term Memory (LSTM) network for predicting pedestrian trajectory in crowded environments, which takes into account the interaction with static and dynamic elements in the scene. Expand
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
TLDR
The proposed Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes significantly improves the prediction accuracy compared to other baseline methods. Expand
Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning
TLDR
This work presents the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments and provides a detailed analysis of HDD with a comparison to other driving datasets. Expand
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
TLDR
A deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time and the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques. Expand
...
1
2
3
4
...