In-car advisory system for lane-changing in a connected vehicle environment

This thesis investigates the potential of in-car advisory systems to suggest location and timing where and when lane-changes should be executed, by evaluating traffic flow conditions with data that is available using vehicle-to-vehicle communication. After investigating existing literature regarding car-following and lane-changing models, as well as driving support assistance systems and vehicle communication applications and practice, a new lane-changing model is introduced, with the objective to serve as a basis for the development of the in-car advisory system. In particular, the model accounts for information about position and speed of vehicles that are downstream from the considered vehicle current position, namely, out of the sight of a driver. Based on the proposed model, a decision system to deliver lane-changing advices to the driver is implemented, with the goal of avoiding or reducing traffic congestion. A set of simulations using the microscopic traffic simulator AIMSUN are performed to test the effectiveness of the proposed system.Outgoin

In-car advisory system for lane-changing in a connected vehicle environment

This thesis investigates the potential of in-car advisory systems to suggest location and timing where and when lane-changes should be executed, by evaluating traffic flow conditions with data that is available using vehicle-to-vehicle communication. After investigating existing literature regarding car-following and lane-changing models, as well as driving support assistance systems and vehicle communication applications and practice, a new lane-changing model is introduced, with the objective to serve as a basis for the development of the in-car advisory system. In particular, the model accounts for information about position and speed of vehicles that are downstream from the considered vehicle current position, namely, out of the sight of a driver. Based on the proposed model, a decision system to deliver lane-changing advices to the driver is implemented, with the goal of avoiding or reducing traffic congestion. A set of simulations using the microscopic traffic simulator AIMSUN are performed to test the effectiveness of the proposed system.Outgoin

Detection of Direct Sun Glare on Drivers from Point Clouds

Sunlight conditions can reduce drivers’ visibility, which is a safety concern on road networks. This research introduces a method to study sun glare incidence in road environments. Sun glare areas during daylight hours are automatically detected from mobile laser scanning (MLS) and aerial laser scanning (ALS) point clouds. The method comprises the following steps. First, the Sun’s position (solar altitude and azimuth) referring to a location is calculated. Second, the incidence of sun glare with the user’s angle of vision is analyzed based on human vision. Third, sun ray intersections with near obstacles (vegetation, building, etc.) are calculated utilizing MLS point clouds. Finally, intersections with distant obstacles (mountains) are calculated utilizing ALS point clouds. MLS and ALS data are processed in order to combine both data types, remove outliers, and optimize computational time for intersection searches (point density reduction and Delaunay triangulation). The method was tested on two real case studies, covering roads with different bearings, slopes, and surroundings. The combination of MLS and ALS data, together with the solar geometry, identify areas of risk for the visibility of drivers. Consequently, the proposed method can be utilized to reduce sun glare, implementing warnings in navigation systems

Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis

ABSTRACTMobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments