Human Performance in Critical Scenarios as a Benchmark for Highly Automated Vehicles

Abstract

Before highly automated vehicles (HAVs) become part of everyday traffic, their safety has to be proven. The use of human performance as a benchmark represents a promising approach, but appropriate methods to quantify and compare human and HAV performance are rare. By adapting the method of constant stimuli, a scenario-based approach to quantify the limit of (human) performance is developed. The method is applied to a driving simulator study, in which participants are repeatedly confronted with a cut-in manoeuvre on a highway. By systematically manipulating the criticality of the manoeuvre in terms of time to collision, humans’ collision avoidance performance is measured. The limit of human performance is then identified by means of logistic regression. The calculated regression curve and its inflection point can be used for direct comparison of human and HAV performance. Accordingly, the presented approach represents one means by which HAVs’ safety performance could be proven.

The Effect of Time Window Length on EEG-Based Emotion Recognition

“…EEG signals also have a relatively low signal-to-noise ratio (SNR) and are susceptible to distortion from artificial interference (e.g., eye movement) [28]. To remove these artifacts and make EEG signals more correlated to the target events, EEG signals are usually calculated in five frequency bands, i.e., delta (1-3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta (14-30 Hz), and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) [27]. Useful features can be extracted from these five frequency bands of EEG signals for detailed analysis on specific tasks [29,30].…”

“…Among the various physiological signals, electroencephalogram (EEG) has been frequently reported to be closely and directly related with human emotions in previous studies [6][7][8][9][10]. George et al [11] applied fast Fourier transformation (FFT) and frequency bandpass to extract features from EEG signals and performed emotion recognition in valence and arousal dimensions with a support vector machine (SVM).…”

Performance Evaluation Method for Automated Driving System in Logical Scenario

“…For AVs, high-level intelligence is indicated by a high-degree consistency between the system driving and human driving process. Studies on the similarity between human and AV driving have chosen specific parameters, such as the type of operation taken by the ADS and human [28], and the TTC with the front vehicle when the operation is taken [22]. However, these parameters are difficult to generalize in different scenarios and can only describe the similarity at a specific state rather than over the whole driving process.…”

“…The similarity at specific locations is mostly evaluated without considering the overall driving trajectory. Quante et al used human driving behavior as a benchmark to consider the performance of the measured ADS in hazardous conditions [22]. Since the invention of robotics, the comparative analysis of machine and human intelligence has become a research hotspot.…”

An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling

“…In particular, autonomous driving technology could allow humans to share control with intelligent vehicles [ 10 , 11 , 12 , 13 , 14 , 15 ]. A well-designed co-pilot system requires the vehicle to understand the behavior and state of the driver [ 16 , 17 , 18 , 19 , 20 , 21 ]. Owing to the low costs and wide application of dash cameras, this paper proposes an integrated multi-state driver monitoring framework based on appearance, which does not use intrusive sensors.…”