Search Results for author: Ran Wei

Found 16 papers, 7 papers with code

Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference

no code implementations10 Nov 2023 Johan Engström, Ran Wei, Anthony McDonald, Alfredo Garcia, Matt O'Kelly, Leif Johnson

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles.

Autonomous Vehicles

A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning

no code implementations10 Oct 2023 Ran Wei, Nathan Lambert, Anthony McDonald, Alfredo Garcia, Roberto Calandra

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment.

Model-based Reinforcement Learning

Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

1 code implementation27 Apr 2023 Zihao Li, Pan Gao, Hui Yuan, Ran Wei, Manoranjan Paul

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud.

3D Object Classification 3D Part Segmentation +3

An active inference model of car following: Advantages and applications

no code implementations27 Mar 2023 Ran Wei, Anthony D. McDonald, Alfredo Garcia, Gustav Markkula, Johan Engstrom, Matthew O'Kelly

We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models.

Decision Making

A Light-Weight Communication-Efficient Data Sharing Approach in 5G NR V2X

no code implementations19 Jan 2023 Ran Wei, Lyutianyang Zhang

Timeliness of information is critical for Basic Safety Messages (BSMs) in Vehicle-to-Everything (V2X) communication to enable highly reliable autonomous driving.

Autonomous Driving Scheduling

Dynamic Local Feature Aggregation for Learning on Point Clouds

1 code implementation7 Jan 2023 Zihao Li, Pan Gao, Hui Yuan, Ran Wei

Existing point cloud learning methods aggregate features from neighbouring points relying on constructing graph in the spatial domain, which results in feature update for each point based on spatially-fixed neighbours throughout layers.

Point Cloud Classification Position

Performance Analysis of Semi-Persistent Scheduling Throughput in 5G NR-V2X: A MAC Perspective

no code implementations23 Aug 2022 Ran Wei

Subsequently, the average throughput as a function of distance in the partially connected vehicular network is analyzed.


Electric vehicle demand estimation and charging station allocation using urban informatics

no code implementations Transportation Research Part D: Transport and Environment 2022 Zhiyan Yi, Xiaoyue Cathy Liu, Ran Wei

The results are fed into the capacitated maximal coverage location problem (CMCLP) model to optimize the spatial layout of public charging stations by maximizing their utilization.

Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments

1 code implementation11 Apr 2020 Ziwei Liao, Wei Wang, Xianyu Qi, Xiao-Yu Zhang, Lin Xue, Jianzhen Jiao, Ran Wei

As objects are often observed locally, the proposed algorithm uses the symmetrical properties of indoor artificial objects to estimate the occluded parts to obtain more accurate quadric parameters.

object-detection Object Detection

PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study

no code implementations1 Apr 2019 Mehrdad Shoeiby, Antonio Robles-Kelly, Ran Wei, Radu Timofte

This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution.

Image Super-Resolution

Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

no code implementations SEMEVAL 2018 Zewen Chi, He-Yan Huang, Jiangui Chen, Hao Wu, Ran Wei

This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets.

Sentence Classification Sentiment Analysis

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