Search Results for author:
Found 8 papers, 1 papers with code
Integrating Large Language Models for Severity Classification in Traffic Incident Management: A Machine Learning Approach
Incorporating features from language models with those directly obtained from incident reports has shown to improve, or at least match, the performance of machine learning techniques in assigning severity levels to incidents, particularly when employing Random Forests and Extreme Gradient Boosting methods.
Traffic Accident Risk Forecasting using Contextual Vision Transformers
This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem.
Traffic incident duration prediction via a deep learning framework for text description encoding
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents.
Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks
Forecasting the trajectory of pedestrians in shared urban traffic environments is still considered one of the challenging problems facing the development of autonomous vehicles (AVs).
Fast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images
In this paper, we present a novel method to simulate LiDAR point cloud with faster rendering time of 1 sec per frame.
Refined Continuous Control of DDPG Actors via Parametrised Activation
In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behaviour discrepancy of different actuators, under different load conditions during interaction with the environment.
Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data
We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors.
Ranked #2 on
Unsupervised Domain Adaptation
on PreSIL to KITTI
Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet
We trained and evaluated our framework based on real data collected from urban traffic environments.
