# Improving Vision-based Self-positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection

## TL;DR

This paper presents a Bayesian framework for vehicle self-positioning using dashboard camera videos, integrating lane and vehicle detection to improve accuracy and computational efficiency in intelligent transportation systems.

## Contribution

It introduces a novel integrated approach combining lane and vehicle detection within a Bayesian framework for self-positioning, enhancing accuracy and efficiency.

## Key findings

- Bounding box proposal reduction by a factor of 6
- Significant decrease in false detections
- Acceptable results on real-world videos

## Abstract

Traffic congestion is a widespread problem. Dynamic traffic routing systems and congestion pricing are getting importance in recent research. Lane prediction and vehicle density estimation is an important component of such systems. We introduce a novel problem of vehicle self-positioning which involves predicting the number of lanes on the road and vehicle's position in those lanes using videos captured by a dashboard camera. We propose an integrated closed-loop approach where we use the presence of vehicles to aid the task of self-positioning and vice-versa. To incorporate multiple factors and high-level semantic knowledge into the solution, we formulate this problem as a Bayesian framework. In the framework, the number of lanes, the vehicle's position in those lanes and the presence of other vehicles are considered as parameters. We also propose a bounding box selection scheme to reduce the number of false detections and increase the computational efficiency. We show that the number of box proposals decreases by a factor of 6 using the selection approach. It also results in large reduction in the number of false detections. The entire approach is tested on real-world videos and is found to give acceptable results.