Since long test roads are usually required for investigating tire running behaviors, which lead to large-scale simulation models and time consuming problems, the alternately moving road method is proposed to handle this problem.
The use of on-road methods to accurately describe the behaviours occurring in everyday real-world conditions, to quantify risks for safety critical events, and an improved understanding of the factors that contribute to risk, clearly has huge potential to promote further road trauma reductions.
Finally, as current road construction methods require a large amount of maintenance, any improved construction methods including bio-engineered roads with still require maintenance yet to a lesser extent (Howell, 1999).
Many traditional road evaluation methods utilize measurements taken in situ along with visual examinations and interpretations.
Inverse perspective mapping and road segmentation methods are used as preprocessing in [20, 21].
Deep cold recycling with foamed bitumen (FB-RCM) is currently one of the most widely used road rehabilitation methods.
Road extraction methods can be classified into two categories such as semi-automatic and fully automatic [5].
Once problem areas are identified, specific conditions may then require the use of traditional road assessment methods.
The road detection methods which are requiring human interaction are classified as semi-automatic, and those that are not requiring human interaction are known as automatic [6].
Comparing the proposed approach with two state-of-the-art image space clustering-based road vectorization methods proves its efficiency to deal with roads with different widths, parallel roads with different distances, different types of intersections, and also noise clusters.
Going beyond technology road mapping methods (TRMs) to adopt the market pull strategy of technology-product integration, Caetano and Amaral (2011) proposed a method for the organizations such as SMEs and research centers.
