Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network

Kosaraju

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

et al. 2019

821

798

This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with a physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.

Section: Related Workmentioning

confidence: 99%

SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints

Kosaraju

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

et al. 2019

821

798

“…The occupancy map representation expresses the multimodality and uncertainty of future actor motion by creating a spatially discretized grid around the actor. Each of the grid cells estimates the probability of the actor occupying this cell at a particular time-point [17], [18], [19]. Both representations, continuous in the spatial dimensions or not, are discrete temporally, which may lead to suboptimal performance in many real world applications where the actors usually behave smoothly.…”

Section: Related Workmentioning

confidence: 99%

Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization

Su¹,

Wang²,

Cui³

et al. 2020

Preprint

A commonly-used representation for motion prediction of actors is a sequence of waypoints (comprising positions and orientations) for each actor at discrete future timepoints. While this approach is simple and flexible, it can exhibit unrealistic higher-order derivatives (such as acceleration) and approximation errors at intermediate time steps. To address this issue we propose a simple and general representation for temporally continuous probabilistic trajectory prediction that is based on polynomial trajectory parameterization. We evaluate the proposed representation on supervised trajectory prediction tasks using two large self-driving data sets. The results show realistic higher-order derivatives and better accuracy at interpolated time-points, as well as the benefits of the inferred noise distributions over the trajectories. Extensive experimental studies based on existing state-of-the-art models demonstrate the effectiveness of the proposed approach relative to other representations in predicting the future motions of vehicle, bicyclist, and pedestrian traffic actors.

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

“…Moreover, these methods only predict behaviors for a certain entity. A few works also took advantage of both recurrent neural networks [5], [6] and generative modeling to learn an explicit or implicit trajectory distribution, which achieved better performance [7]- [9]. However, they either leveraged only static context images or only trajectories of agents, which is not sufficient to make predictions for the agents that interact with both static and dynamic obstacles.…”

Section: Trajectory and Sequence Predictionmentioning

confidence: 99%

Conditional Generative Neural System for Probabilistic Trajectory Prediction

Tomizuka

2019

146

Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control. Due to the uncertain nature of the future, it is desired to make inference from a probability perspective instead of deterministic prediction. In this paper, we propose a conditional generative neural system (CGNS) for probabilistic trajectory prediction to approximate the data distribution, with which realistic, feasible and diverse future trajectory hypotheses can be sampled. The system combines the strengths of conditional latent space learning and variational divergence minimization, and leverages both static context and interaction information with soft attention mechanisms. We also propose a regularization method for incorporating soft constraints into deep neural networks with differentiable barrier functions, which can regulate and push the generated samples into the feasible regions. The proposed system is evaluated on several public benchmark datasets for pedestrian trajectory prediction and a roundabout naturalistic driving dataset collected by ourselves. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction accuracy.