Convolutional Social Pooling for Vehicle Trajectory Prediction

Deo, Nachiket; Trivedi, Mohan M.

doi:10.48550/arxiv.1805.06771

Cited by 15 publications

(46 citation statements)

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“…Learning-based methods for various prediction tasks. Many learning-based prediction methods follow an RNN encoder-decoder structure [16] and use it for predicting the trajectory of pedestrians [17]- [19] and vehicles [2,18]. RNNs are an alternative to the traditional methods (e.g., SVM, Gaussian process) [20,21] for capturing maneuver patterns.…”

Section: Related Workmentioning

confidence: 99%

“…1. In [9], Alahi et al proposed a learning-based social pooling strategy for modeling pedestrian interactions, which became very popular and was later applied to vehicles [2]. However, using their methods agents appearing in the same spatial location will be weighted equally in spite of their different dynamics, which is problematic for a highly dynamic driving scenario, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network

Ding

Chen

Shen

2019

2019 International Conference on Robotics and Automation (ICRA)

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Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles. Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. To avoid unsatisfactory reactive decisions, it is essential to count long-term future rewards in planning, which requires extending the prediction horizon. In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern. 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 states and connection feature of each interaction pair. The output of our method is a probabilistic likelihood of multiple behavior classes, which matches the multimodal and uncertain nature of the distant future. A systematic comparison of our method against two state-of-the-art methods and another two baseline methods on a publicly available real highway dataset is provided, showing that our method has superior accuracy and advanced capability for interaction modeling.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network

Ding

Chen

Shen

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…There are variety of works dealing with trajectory prediction problems for road entities such as vehicles and pedestrians. [14] proposed a Long Short-Term Memory (LSTM) encoder-decoder model to predict a multi-modal predictive distribution over future trajectories based on maneuver classes. [15] applied the Hidden Markov Model (HMM) to predict the trajectories for individual driver.…”

Section: Trajectory Predictionmentioning

confidence: 99%

Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model

Zhan

Sun

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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For autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels since potentially dangerous situations could be avoided in advance. While impressive results have been shown on predicting each agent's behavior independently, we argue that it is not valid to consider road entities individually since transitions of vehicle states are highly coupled. Moreover, as the predicted horizon becomes longer, modeling prediction uncertainties and multi-modal distributions over future sequences will turn into a more challenging task. In this paper, we address this challenge by presenting a multi-modal probabilistic prediction approach. The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents. Most importantly, our model is interpretable, which can explain the underneath logic as well as obtain more reliability to use in real applications. A complicate real-world roundabout scenario is utilized to implement and examine the proposed method.

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“…B. Related Works 1) Learning-based Approach: Learning-based methods [1]- [7] have been wildly used for prediction problems for autonomous vehicles, which utilize real data to produce the future outcomes of human drivers. In [2], the authors modeled the driver behavior by hidden Markov models (HMM) and Gaussian Process (GP) to generate a group of future trajectories of the predicted vehicle.…”

Section: A Motivationmentioning

confidence: 99%

“…In [2], the authors modeled the driver behavior by hidden Markov models (HMM) and Gaussian Process (GP) to generate a group of future trajectories of the predicted vehicle. The long shortterm memory (LSTM) method is utilized in [1] and [3] to analyze past trajectory data and predict the future locations of the surrounding vehicles. [8] proposed to combine a modified mixture density network (MDN) [4] and a conditional variational autoencoder (CVAE) to predict both discrete intention and continuous motions for multiple interacting vehicles.…”

Section: A Motivationmentioning

confidence: 99%

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

Sun

Tomizuka

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module. The prediction module is also expected to generate reasonable results in the presence of unseen and corner scenarios. Two types of prediction models are typically used to solve the prediction problem: learning-based model and planning-based model. Learning-based model utilizes real driving data to model the human behaviors. Depending on the structure of the data, learning-based models can predict both rational and irrational behaviors. But the balance between them cannot be customized, which creates challenges in generalizing the prediction results. Planning-based model, on the other hand, usually assumes human as a rational agent, i.e., it anticipates only rational behavior of human drivers. In this paper, a generic prediction architecture is proposed to address various rationalities in human behavior. We leverage the advantages from both learningbased and planning-based prediction models. The proposed approach is able to predict continuous trajectories that wellreflect possible future situations of other drivers. Moreover, the prediction performance remains stable under various unseen driving scenarios. A case study under a real-world roundabout scenario is provided to demonstrate the performance and capability of the proposed prediction architecture.

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Convolutional Social Pooling for Vehicle Trajectory Prediction

Cited by 15 publications

References 0 publications

Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network

Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network

Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

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