Impact of Traffic Lights on Trajectory Forecasting of Human-driven Vehicles Near Signalized Intersections

Oh, Geunseob; Peng, Huei

doi:10.48550/arxiv.1906.00486

Cited by 3 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To learn and train the model, we used the Waterloo multi-agent traffic intersection dataset [14]. Waterloo is an approximately one-hour video dataset of aerial views of a crowded urban intersection in Waterloo, Canada.…”

Section: Experiments 41 Datasetmentioning

confidence: 99%

Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light

Lee,

Park,

You

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Trajectory prediction is essential for the safe driving of autonomous vehicles. With the advancement of advanced sensors and deep learning technologies, attempts have been made to reflect complex interactions. In this study, we propose a deep learning-based Multimodal Trajectory Prediction method that reflects traffic light conditions in complex urban intersection situations. Based on existing state-of-the-art research, the multi-path of multi-agents was predicted using a generative model, and the actor’s trajectory information, state, social interaction, and traffic light state, and scene context were reflected. Performance evaluation was conducted using metrics commonly used to evaluate the performance of stochastic trajectory prediction models. This study is meaningful in that trajectory prediction was performed by reflecting realistic elements of traffic lights in a complex urban environment. Future research will need to be conducted on efficient ways to reduce time and computational performance while reflecting different real-world environments.

show abstract

Section: Experiments 41 Datasetmentioning

confidence: 99%

Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light

Lee,

Park,

You

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…A common choice for the density model is a mixture of Gaussians where the neural network estimates the parameters of the Gaussian mixture. MDN with Gaussian mixtures have been used for prediction tasks [3,14,23]. Since MDN works with a pre-determined number of modes, a MDN(GMM)-based model may sacrifice the generality.…”

Section: Related Workmentioning

confidence: 99%

“…It is not trivial for a prediction model to satisfy all four attributes. First of all, not all neural-network models are probabilistic unless they build on explicit density models such as Mixture Density Network (e.g., Gaussian Mixture Models) [2,3], Normalizing-flow models [4,5], Variational Autoencoder (VAE) [6]. Secondly, not all probabilistic models are multi-modal, for instance, a uni-modal Gaussian.…”

Section: Introductionmentioning

confidence: 99%

CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving

Oh¹,

Peng²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-modal, context-driven, and general. We present Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a conditional VAE that extensively leverages hypernetwork and performs generative tasks for high-dimensional problems like the prediction task. We first evaluate CVAE-H on simple generative experiments to show that CVAE-H is probabilistic, multimodal, context-driven, and general. Then, we demonstrate that the proposed model effectively solves a self-driving prediction problem by producing accurate predictions of road agents in various environments.

show abstract

A Data-driven, Falsification-based Model of Human Driver Behavior

Sohani¹,

Wang

2019

Preprint

View full text Add to dashboard Cite

Impact of Traffic Lights on Trajectory Forecasting of Human-driven Vehicles Near Signalized Intersections

Cited by 3 publications

References 19 publications

Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light

Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light

CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving

A Data-driven, Falsification-based Model of Human Driver Behavior

Contact Info

Product

Resources

About