Vehicle Prediction

Vehicle prediction focuses on accurately forecasting the future movements and attributes of vehicles, crucial for applications like autonomous driving and used car pricing. Current research emphasizes developing robust models that incorporate diverse data sources (images, text, sensor data) and account for uncertainties in predictions, employing techniques like probabilistic regression, boosting algorithms, graph convolutional networks, and attention mechanisms to improve accuracy and reliability. These advancements are significant for enhancing the safety and efficiency of autonomous systems and improving the transparency and fairness of automated valuation processes.

Papers

March 6, 2024

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme
Price Prediction Tabular Prediction Pricing Algorithm Vehicle Prediction

July 29, 2023

Vehicle Price Prediction By Aggregating decision tree model With Boosting Model
Auwal Tijjani Amshi
Decision Tree Predictive Model Prediction Method Boosting Algorithm Price Prediction Vehicle Prediction

July 21, 2023

Safety-Assured Speculative Planning with Adaptive Prediction
Xiangguo Liu, Ruochen Jiao, Yixuan Wang, Yimin Han, Bowen Zheng, Qi Zhu
Autonomous Driving Autonomous Vehicle Novel Planning Adaptive Prediction Vehicle Prediction

May 24, 2023

Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data
Hanqi Su, Binyang Song, Faez Ahmed
Text Modality Multi Modal Model Multi Modal Learning Parametric Model Multimodal Learning Method Multi Modal Machine Learning Vehicle Prediction

September 26, 2022

Exploring Attention GAN for Vehicle Motion Prediction
Carlos Gómez-Huélamo, Marcos V. Conde, Miguel Ortiz, Santiago Montiel, Rafael Barea, Luis M. Bergasa
Motion Prediction Autonomous Driving Stack Vehicle Motion Prediction Argoverse Motion Forecasting Attention GAN Vehicle Prediction

February 9, 2022

CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention
Julian Schmidt, Julian Jordan, Franz Gritschneder, Klaus Dietmayer
Autonomous Vehicle Graph Convolution Multi Head Self Attention Trajectory Prediction Model Vehicle Trajectory Prediction Vehicle Prediction Crystal Graph Convolutional Neural Network