Abstract
Road departure is one of the main causes of single vehicle and frontal crashes. By implementing lateral support systems, a significant amount of these accidents can be avoided. Typical accidents are normally occurring due to unintentional lane departure where the driver drifts towards and across the line identifying the edge of the lane. The Lane Support Systems (LSS) uses cameras to “read” the lines on the road and alert the driver if the car is approaching the lines. Anyway, despite the assumed technology readiness, there is still much uncertainty regarding the needs of vision systems for “reading” the road and limited results are still available from in field testing. In such framework the paper presents an experimental test of LSS performance carried out in two lane rural roads with different geometric alignments and road marking conditions. LSS faults, in day light and dry pavement conditions, were detected on average in 2% of the road sections. A decision tree method was used to analyze the cause of the faults and the importance of the variable involved in the process. The fault probability increased in road sections with radius less than 200 m and in poor conditions of road marking.
Highlights
Advanced driver assistance systems (ADAS) support drivers to maintain a safe speed and distance [1], to drive within the lane, to avoid obstacles in an increasingly complex driving environment
Intelligence (AI) are used by ADAS to detect a pavement marking and the main feature in the digital image is the contrast between the intensity of marking pixels and the road’s pixels
Since our objective was to identify specific features which explain the change in the response of Lane Support Systems (LSS), we introduced a posterior classification ratio (PCR) to assign response class to each node of the tree, instead of the mode
Summary
Advanced driver assistance systems (ADAS) support drivers to maintain a safe speed and distance [1], to drive within the lane, to avoid obstacles in an increasingly complex driving environment. Studies on the safety effects of such systems show a high potential. According to eImpact Project [2], Speed Alert (with active gas pedal) is expected to reduce by 5% road crash fatalities and injuries and Lane Keeping Support by 3%. It is evident that the full potential of the new technologies will only become reality with large-scale deployment in vehicles. Based on the definition given by SAE Standard J3016 [3] the: . Tral with regard to jurisdictional claims in published maps and institutional affiliations.
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