Evaluation of driver visual demand in complex two dimensional rural highway alignments

Transportation has proven to be one of the most important infrastructures in the economic development of any country. Safe and effective traffic operations support growth of the economy and help in future developments. Highway alignment design plays a crucial role in implementing safer traffic operation and management. Road accidents not only jeopardize safety, but also have a major effect on the national economy. These accidents can be divided in three classes, grouped according to their severity. Statistics in North America and Europe show that one of the major reasons for such road accidents is driver error. Wrong decisions during navigation may be the primary reason for such errors. Wrong decisions occur when a driver is unable to process the range of visual information available in a complex highway situation. Drivers need to have sufficient visual information in guiding and controlling vehicles along the correct path. Drivers scan the roadway to collect visual information. This visual information consists mainly of the traffic situation, roadway signs, and the information from the highway alignment itself. The information from the highway alignment plays a major role in decision-making during maneuvering. All drivers, therefore, need sufficient visual information for perfect navigating, and for guiding and controlling their vehicles on the road. The main focus of this research study was on evaluating visual demands on two-dimensional highway alignments with an emphasis on determining the effect of complex curves on visual demand. Complex curves are defined as combinations of simple, compound, and reverse curves in a series. Eighteen hypothetical alignments for two-lane rural highways have been developed following the standard guidelines of the Transportation Association of Canada (TAC) and American Association of State Highway Transportation Officials (AASHTO). These alignments were simulated in a low-cost driving simulator. A series of experiments was carried out using the visual occlusion method. Nine subject drivers drove in the simulator, and the output data related to visual demand information and positioning of the subject vehicle were connected. The data relating to visual demand information and lateral positioning on curves and tangents were processed using Microsoft ExceFM and analyzed using SAS, a statistical software. The turning directions, characteristics of preceding elements, and the combination of curve to curve, tangent to curve, or curve to tangent have been considered as nominal variables and analyzed as independent variables with visual demand. It has been observed that visual demand varies widely with the inverse of radius of curvature of the preceding and current elements, and the characteristics of the combination of the current and the preceding element. Visual demand also varies on identical tangents, depending on the deflection angle, inverse of radius, and turning direction of the preceding curve. The standard deviation of lateral positioning of the subject vehicle was evaluated with respect to the centre-line of the driving lane. This was supposed to have a considerable impact on visual demand evaluation, but it has been observed that this does not bear any significant relationship to visual demand. In addition to curves, tangents, as preceding elements have an immense impact on visual demand evaluation on following curves. Besides, visual demand on tangents has also been observed as highly dependent on the preceding curve and their turning directions.

Evaluation of driver visual demand in complex two dimensional rural highway alignments

Transportation has proven to be one of the most important infrastructures in the economic development of any country. Safe and effective traffic operations support growth of the economy and help in future developments. Highway alignment design plays a crucial role in implementing safer traffic operation and management. Road accidents not only jeopardize safety, but also have a major effect on the national economy. These accidents can be divided in three classes, grouped according to their severity. Statistics in North America and Europe show that one of the major reasons for such road accidents is driver error. Wrong decisions during navigation may be the primary reason for such errors. Wrong decisions occur when a driver is unable to process the range of visual information available in a complex highway situation. Drivers need to have sufficient visual information in guiding and controlling vehicles along the correct path. Drivers scan the roadway to collect visual information. This visual information consists mainly of the traffic situation, roadway signs, and the information from the highway alignment itself. The information from the highway alignment plays a major role in decision-making during maneuvering. All drivers, therefore, need sufficient visual information for perfect navigating, and for guiding and controlling their vehicles on the road. The main focus of this research study was on evaluating visual demands on two-dimensional highway alignments with an emphasis on determining the effect of complex curves on visual demand. Complex curves are defined as combinations of simple, compound, and reverse curves in a series. Eighteen hypothetical alignments for two-lane rural highways have been developed following the standard guidelines of the Transportation Association of Canada (TAC) and American Association of State Highway Transportation Officials (AASHTO). These alignments were simulated in a low-cost driving simulator. A series of experiments was carried out using the visual occlusion method. Nine subject drivers drove in the simulator, and the output data related to visual demand information and positioning of the subject vehicle were connected. The data relating to visual demand information and lateral positioning on curves and tangents were processed using Microsoft ExceFM and analyzed using SAS, a statistical software. The turning directions, characteristics of preceding elements, and the combination of curve to curve, tangent to curve, or curve to tangent have been considered as nominal variables and analyzed as independent variables with visual demand. It has been observed that visual demand varies widely with the inverse of radius of curvature of the preceding and current elements, and the characteristics of the combination of the current and the preceding element. Visual demand also varies on identical tangents, depending on the deflection angle, inverse of radius, and turning direction of the preceding curve. The standard deviation of lateral positioning of the subject vehicle was evaluated with respect to the centre-line of the driving lane. This was supposed to have a considerable impact on visual demand evaluation, but it has been observed that this does not bear any significant relationship to visual demand. In addition to curves, tangents, as preceding elements have an immense impact on visual demand evaluation on following curves. Besides, visual demand on tangents has also been observed as highly dependent on the preceding curve and their turning directions.

An Analysis on the Types of Formulating Association Standards for General Drawing of Highway Engineering Design for Building Transportation Strength

This paper studies the classification of association standards of the general drawings of highway engineering design through investigation and analysis and sets up the system of the association standards. The classification management of general drawing standard system is carried out through research. The ten categories of general drawing management are proposed including general design, highway alignment, subgrade, pavement, bridge and culvert, tunnel, road intersection, traffic engineering and road facilities, environmental protection and landscape design, other engineering, road construction materials.

Visibility-Based Technologies and Methodologies for Autonomous Driving

The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles.