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In order to reduce the amount of EEG collected by the fatigue driving monitoring system, this paper proposes a new EEG monitoring method based on compressed sensing. According to the driver's driving duration, the new proposed method adopts different sampling rate to compressive sample the EEG of the driver in different driving stages, and then realizes the accurate reconstruction of the compressed EEG data in the computer of vehicle. The experiment verifies effectiveness of the proposed method by collecting EEG signals from multiple subjects during simulated driving. The experimental results show that, compared with traditional sampling, the new method reduces the amount of sampled data by 3 orders of magnitude. And when the compression ratio is not higher than 70%, the reconstruction error is less than 0.16. [ABSTRACT FROM AUTHOR]

• Investigated mental workload and fatigue of the drivers using EEG. • Conducted experiments in authentic driving environments involving 39 participants. • Revealed higher mental workload on local street with heavy traffic and highway. • Mental fatigue index was correlated with the mental workload indexes. • Established that drivers with more experience exhibited lower mental workload. The safety and performance of automobile drivers depend on many factors. The mental status of the drivers is the foremost factor in ensuring driving safety, in addition to physical elements. Studies with drivers are generally conducted in driving simulators or with scenarios close to actual driving. This study investigated the mental workload of drivers by analyzing electroencephalography data recorded in totally spontaneous real driving tasks, to determine the effect of different road conditions and driving experience. Two mental workload indexes (Frontal Theta/Parietal Alpha and Frontal Midline Theta) and a mental fatigue index (Alpha + Theta/Beta) were calculated using the band powers. Drivers were found to experience a higher mental workload in road sections with heavy traffic and variable road parameters by analyzing EEG data in real traffic. The correlation coefficients between the fatigue index and the two workload indexes were found to be 0.577 and 0.678, respectively. The workload decreased with increasing driving experience. Therefore, having experienced drivers perform commercial driving tasks can ensure safer driving. By employing novel methods to handle real-world EEG data, autonomous driving systems can also benefit. [ABSTRACT FROM AUTHOR]

Over forty thousand people die in motor vehicle crashes in the United States each year, and over two million are injured. The careful deployment Of driving automation systems could prevent many of these deaths and injuries, but only if it is accompanied by effective regulation. Conventional vehicle safety standards are inadequate because they can only test how technology performs in a controlled environment. To assess the safety of a driving automation system, regulators must observe how it performs in a range of unpredictable, real world edge cases. The National Highway Traffic Safety Administration (NHTSA) is trying to adapt by experimenting with a novel regulatory strategy. Instead of setting standards, the agency is using its statutory powers in unprecedented ways-ordering automation developers to report crashes daily and directing rapid recalls that require changes to defective software. NHTSA is betting that intense monitoring and the credible threat of recalls will push developers to prioritize safety. This Article argues that NHTSA's experimental strategy could be transformed into effective safety regulation. Regulators should (1) require that all new vehicles be equipped with telematics that can send safety data and receive software updates over the air: (2) mandate universal crash reporting; and (3) use recalls to force developers of driving automationsystems that create unreasonable risks to restrict where their systems can operate until they can develop safer code. [ABSTRACT FROM AUTHOR]

Trade-in program is a widely used way for recycling waste products. Similarly, a large number of automobile producers have launched trade-in-for new fuel cars or trade-in-for new energy vehicles to replacement consumers. We consider a two-period dynamic game problem and analyze the impact of the consumer anxiety behavior and the policy substitution effect on the customers' selection behavior, and the automobile producer's optimal pricing strategy. The results show that in the basic model, short-sighted customers' choices are diverse. In addition, both the consumer anxiety behavior and the policy substitution effect influence the producer's profit. In the extension models, it exists a threshold of the additional innovation level. When the additional innovation level is larger than the threshold value, automobile producers adopt the dynamic pricing strategy. Otherwise, they adopt the preannounce pricing strategy. Moreover, it exists a threshold of the additional innovation level. When it is larger than the threshold value, automobile producers adopt the single rollover strategy. Otherwise, they adopt the dual rollover strategy. [ABSTRACT FROM AUTHOR]