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Objective：The moral and ethical issue is a great challenge to the development of autonomous vehicles. There may be distinctions between the choices made by an observer and a participant. The paper is designed to investigate whether drivers will sacrifice the fewest people to save more people in social dilemma, and whether human drivers would give priority to protecting pedestrians or self-protection in an emergency. Methodology: The experiment was conducted with a total of 50 participants assigned to three groups. Three experimental scenarios were designed and each of them contained a social dilemma. A driving simulator was used in this study to explore the choices of human drivers in social dilemma. In addition, the simulator results were compared with those of questionnaire survey. Result: In study 1, 73% of 22 participants swerved into the right lane to hit only one pedestrian for the safety of other five. In study 2 and 3, more participants chose to hit the barrier to protect the pedestrian. Conclusion: A conclusion can be drawn from the second and third group of experiments that most drivers consider not only their own safety, but the safety of pedestrians. Most of the participants intended to minimize the total amount of harm in social dilemma. The choice of crashing into barriers to protect a pedestrian can also be seen as a way to minimize the total amount of harm.

Driving characteristics of lane departure can provide design principles for lane departure warning system and lane keeping assist system. In this article, based on driving simulator experiments of unintentional lane departure, the operation performances of human drivers, the motion characteristics of vehicles, and the relative motion between the vehicle and lane line are synthetically studied. First, unintentional lane departure is classified into lane departure by fatigue and lane departure by secondary task. Subsequently, two simulator experiments of fatigue-based lane departure and secondary task–based are designed and performed to collect the synchronous driver–vehicle–road data. The data of steering angle, steering angle velocity, steering angle entropy, lateral acceleration, lateral velocity, and yaw velocity, under fatigue-based and secondary task–based lane departure are collected and compared with those gathered under normal lane changing. Results show that the characteristics of unintentional lane departures differ from that of normal lane departure changing. Furthermore, the characteristics of fatigue-based lane departures are shown some differences with that of secondary task–based ones.

We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.