Investigation of collision estimation with vehicle and pedestrian using CARLA simulation software

Abstract

The effectiveness of object detection systems in diverse driving environments is crucial in the growing field of automotive safety. The increasing frequency of traffic accidents, especially at busy intersections with heavy traffic and limited visibility, highlights the pressing requirement for advanced vehicle detection systems. Prior to implementing the real-time experiment, it is advisable first to conduct a simulation in order to gain a deeper understanding of the practical implementation in real-time scenarios. On the other hand, this approach has the potential to reduce both time and cost significantly. The system utilised a software-based solution by implementing the CARLA simulator. This study aims to analyse vehicle detection at T-junctions, cross-junctions, and roundabouts using image data obtained from the CARLA platform. Subsequent analysis differentiates between vehicles and non-vehicle objects in the dataset. The model concludes by proposing Python-based integrative solutions to enhance object detection systems for diverse roads and atmospheric situations. The significance of this study is evaluating the probability of accidents by tracking key factors like vehicle speed, distance, and density on various road types. In future research, it will be essential to investigate how different weather conditions, including rain, haze, and low-light scenarios, affect on sensor performance, specifically LiDAR sensors. Advanced machine learning techniques are proposed to evaluate the effectiveness of the vehicle detection system in collecting key parameters like vehicle count, speed, and distance in junction and roundabout scenarios. These findings have important implications for the advancement of more efficient, context-aware detection systems in the automotive sector.