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Whereas the development of ADAS seeks to improve driver's overall performance with a particular focus on traffic safety improvement; with it comes the requirement and opportunity to objectively evaluate the effectiveness of the technology in improving safety and overall road traffic efficiency. This study evaluates the effectiveness of ADAS in improving driver's risk perception in near-crash events using a novel metric from risk homeostasis theory (RHT) – Safety Margins, as an indicator. By designing a function that captures the initial and maximum risk-level of drivers when involved in a critical driving event while driving on a field operational test (FOT) route in Wuhan China; a comparison of the risk-level of drivers when ADAS was on as against when ADAS was off can be made, enabling an effective evaluation of the impact of ADAS on driver's risk perception and consequent risk mitigation. The results show that ADAS has a positive impact on the low-risk group and moderate-risk group for all drivers, but a negative impact on the high-risk group for skilled drivers. The impact evaluation is done under varying risk-levels of near-crash events with drivers of different driving experiences. [ABSTRACT FROM AUTHOR]

Dangerous driving behavior detection can be used in video surveillance systems to identify dangerous patterns, such as Abrupt Double Lane Change (ALC), Retrograde Driving (RD), and Illegal U-Turn (IT), for traffic design, traffic management, and law enforcement. The purpose of this study is to develop a detection method of dangerous driving behavior based on video surveillance. First, a novel method named trajectory histogram is proposed. A set of trajectory histograms (e.g., control points histogram and velocity histogram) is constructed to represent vehicle motion. Then, a frequently used feature selection method named Minimum Redundancy and Maximum Relevance (mRMR) is introduced to evaluate the most representative trajectory histograms for dangerous driving behavior detection. In addition, a hybrid algorithm, Particle Swarm Optimization-Support Vector Machine (PSO_SVM), is then developed to identify dangerous driving behavior. To validate the performance and effectiveness of the proposed method, several experiments are conducted. The results show that mRMR is better than other representative methods, namely Conditional Mutual Information Maximization (CMIM), Mutual Information Maximization (MIM), and ReliefF, for evaluating the most representative trajectory histograms. Based on the most representative trajectory histograms, the detection accuracy rate of dangerous driving behavior using PSO_SVM is superior to those of the most frequently used classifiers—Naïve Bayesian Classifier (NBC), k-Nearest Neighbor (kNN), and C4.5 decision tree. In addition, we find that the proposed method outperforms the two common approaches for dangerous driving behavior detection in video surveillance systems. Therefore, the proposed method can be widely applied to detect dangerous driving behavior in video surveillance systems. [ABSTRACT FROM PUBLISHER]

Risky driving behavior (RDB) is one of the human factors contributing to the majority traffic injuries and crashes. This paper examines the influence of personality factors on RDB and the mediating role of intention and attitude in the relationship. The influence of different SES, personality, marital status, and gender characteristics on attitude, intention, and risky driving was also examined. A sample of 354 fully licensed Ghanaian drivers, including 278 males and 76 females, participated in the study at two times points separated by three months. The correlation results showed that unhealthy family relationship and negative emotions were positively associated with illegal speed-related behaviors, risky driving, and accident involvement. The results of a structural equation model suggested that personality variables significantly and positively influence intention and attitude toward speeding, with normlessness directly and positively influencing RBD. The mediation analysis shows that intention mediated the effects of personality variables on RBD. No significant mediating effect for attitude was found. The analysis of variance result shows that drivers with high socioeconomic status and personality had the high intention and attitude toward speeding and reported more RDB. Finally, drivers who had been divorced significantly reported more positive attitude toward speeding than the married and single group. Based on the results, the study concludes that, in accessing the effect of personality measures on RDB, it is important to include intention as a possible mediator for more accurate decision. Practical implications for managing different personalities and socioeconomic positions are also discussed. [ABSTRACT FROM AUTHOR]