Driving anger, called “road rage”, has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG) spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β%) and relative energy spectrum of θ band (θ%) for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC) curve analysis, the optimal thresholds (best cut-off points) of β% and θ% for identifying none anger state (i.e., neutral) were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc) of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

Autonomous vehicles (AVs) are a vital direction for intelligent transportation; nevertheless, the current research is insufficient, and the aspects and mechanisms that influence individuals adoption of AVs require additional investigation. This study examines the acceptance of the popularity of AVs from three perspectives: personal-psychological attributes, travel characteristics attributes, and latent variables. The descriptive statistical analysis revealed that the acceptance rate of AVs was 54.6 based on 304 valid questionnaires received through online questionnaires. And the proportion of the 1850 group in the article reached 92.8; thus, this study takes the young group as the object to investigate the acceptance of AVs. A quantitative analysis of each factors impact on the acceptability of AVs was conducted using the hybrid choice model (HCM), which was utilized to observe the link between latent variables. Results from parameter estimates demonstrate that the HCMs fitting impact when latent factors are taken into account is superior to that when latent variables are not taken into account. When latent variables are taken into account, the associated goodness ratio coefficient rises by 0.2337 to 0.2898, which is greater than the model as a whole. The three factors with the highest impact on AV acceptability among the five latent variables were attitude toward use, sense of use gain, and perceived trust, with matching z-test values of 2.42, 2.44, and 2.12, respectively. The development and marketing of AVs by pertinent businesses and government agencies would benefit greatly from this research as a source of reference.

"Road rage", also called driving anger, is becoming an increasingly common phenomenon affecting road safety in auto era as most of previous driving anger detection approaches based on physiological indicators are often unreliable due to the less cons...

“Road rage,” namely, driving anger, has been becoming increasingly common in auto era. As “road rage” has serious negative impact on road safety, it has attracted great concern to relevant scholar, practitioner, and governor. This study aims to propose a model to effectively and efficiently detect driving anger states with different intensities for taking targeted intervening measures in intelligent connected vehicles. Forty-two private car drivers were enrolled to conduct naturalistic experiments on a predetermined and busy route in Wuhan, China, where drivers’ anger can be induced by various incentive events like weaving/cutting in line, jaywalking, and traffic congestion. Then, a data-driven model based on double-layered belief rule base is proposed according to the accumulation of the naturalistic experiments data. The proposed model can be used to effectively detect different driving anger states as a function of driver characteristics, vehicle motion, and driving environments. The study results indicate that average accuracy of the proposed model is 82.52% for all four-intensity driving anger states (none, low, medium, and high), which is 1.15%, 1.52%, 3.53%, 5.75%, and 7.42%, higher than C4.5, BPNN, NBC, SVM, and kNN, respectively. Moreover, the runtime ratio of the proposed model is superior to that of those models except for C4.5. Hence, the proposed model can be implemented in connected intelligent vehicle for detecting driving anger states in real time.

Although some characteristics of physiological signals under different emotions have been studied previously, the differences in electroencephalograms (EEG) between normal driving and angry driving are generally unknown. Moreover, motorists from different countries may have different EEG characteristics because of their different customs, lifestyles, and cultures. Because of this, it is believed that the traffic environment and driving behaviors in China are very different from those in developed countries. Therefore, a study of a motorist's EEG characteristics under angry driving conditions is necessary and beneficial for better understanding the impacts of aggressive driving on traffic safety in China, where "road rage" is common. This study is different from traditional laboratory simulation experiments because ten professional drivers were recruited to drive along a real and particularly busy route in Wuhan. The EEG data and driver anger level scale (reported by the driver himself) were recorded. Study results show that the amplitude of the EEG signal is significantly bigger under angry driving conditions. Furthermore, the mean value of frequency percentage of β waves is significantly larger under angry driving conditions than the value under normal driving conditions. Finally, the mean value of the frequency percentage of δ waves is much smaller under angry driving conditions than the value under normal driving conditions. The results can provide an effective method to distinguish between angry driving and normal driving, which can then provide theoretical support for designing emotion recognition equipment in the future.

Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. I...