Papers | Meng's Page

Do cyclists disregard ‘priority-to-the-right’ more often than motorists?

Wed, 18 Sep 2024 00:00:00 +0000

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Background

Do cyclists disregard traffic rules more often than motorists? Answering this is essential for optimizing traffic safety and efficiency as we transition toward autonomous driving in mixed environments. Grounded in naturalistic observations at urban unsignalized intersections, our study utilizes quasi-experimental methods and regression analysis to quantify compliance behaviors. This research is the result of a seamless collaboration between project managers, sensor technicians, data engineers, and human factors researchers, aiming to provide the critical behavioral data needed for safe human-machine coexistence.

Figure 1. The Collaborative Project Workflow.

Methods

Using stationary cameras, a 12-day naturalistic traffic observation was conducted at an urban T-intersection in Braunschweig, Germany. The video footage underwent spatial calibration to establish a real-world coordinate system. Road users were detected and classified, and their movements were converted into georeferenced trajectories. In 202 interaction cases, a car from the right (Ego, with priority) encountered a car or a bike from the left (Foe, without priority). The study examined how the following factors associated with the violation using logistic regression:

Ego’s turning direction (left vs. right)
Foe’s type (car vs. bike)
Relative arrival time
Foe’s lateral position

Figure 2. Traffic Scenarios: Compliance vs Violation.

Key Findings

Cyclists violated the “priority-to-the-right” rule more frequently than motorists.
Road users with priority were more likely to yield when:
- turning right
- facing a bike
- arriving later at the intersection
- facing a road user close to the lane center

Conclusion

This study underscores the importance of implicit communication in mixed traffic. It provides empirical benchmarks for designing human-like autonomous driving systems, which is supposed to be capable of interpreting and responding to nuanced road-user interactions at unsignalized intersections.

Bringing Emotion Theory into the Cockpit

Sun, 19 Nov 2023 00:00:00 +0000

Background

For nearly a century, the scientific debate on emotion has revolved around two fascinating puzzles. First, the nature of emotion: Are joy and sadness distinct entities, like apples and pears, or merely different shades of the same fruit, like red and green apples? Second, the sequence: Do we run because we feel fear, or do we feel fear because we are running? While the academic community has yet to reach a verdict on these theoretical roots, there is one consensus: to truly understand human emotion, we cannot rely on a single source. It requires multimodal measurement, combining physiological signals, facial expressions, and behavioral data.

This principle is now reshaping the automotive world. From enhancing the passenger’s well-being to solving the complex puzzle of trust in autonomous vehicles, accurate emotion detection is key. Motivated by this, I had the privilege during the early stages of my PhD to independently design and execute a comprehensive multimodal driving simulator experiment, aiming to bridge the gap between abstract theory and real-world application

Figure 1. The Research Workflow.

Hypotheses

Drawing upon the Component Process Model (CPM), the driver’s body responses during fear-inducing scenarios would be modulated by specific cognitive appraisals: Novelty and Power. It’s hypothesized that these cognitive checks manifest through distinct channels:

The Novelty Hypothesis: The appraisal of Novelty (high unexpectedness) will be indicated by immediate responses, specifically an increase in Pupil Diameter, Skin Conductance Level, and the activation of Upper Face Action Units.
The Power Hypothesis: The appraisal of Power (low coping potential/control) will be indicated by a delayed response pattern, specifically an drop in nasal temperature and the activation of Lower Face Action Units.
The Temporal Dynamics Hypothesis: In line with the sequential nature of CPM checks, the facical indicators of Novelty are hypothesized to precede the ones of Power, reflecting the cognitive processing order from event detection to coping evaluation.

Methods

Driving simulator experiments were conducted using Virtual Reality to elicit fear through critical traffic events. Adopting a mixed-methods design, the study integrated objective physiological measurements with subjective assessments. The objective data comprised facial infrared thermography, facial Action Units (AUs), and synchronized peripheral physiological signals. These objective metrics were valided with subjective data collected via the Self-Assessment Manikin (SAM) and the Positive and Negative Affect Schedule (PANAS). This comprehensive approach allowed for the cross-validation of appraisal-driven responses, establishing a robust, multidimensional framework for assessing driver emotions.

Figure 2. Overview of the multi-sensor experimental setup.
The diagram shows the integration of physiological, behavioral, and subjective measurement tools within the simulated driving environment (This image is generated by AI).

Figure 3. Mapping of Facial Action Units (AUs) to basic emotion categories.

Key Findings

The studies empirically validated the Component Process Model (CPM) as a robust framework for assessing driver emotions.
Thermal Imaging as an indicator for “Power”
- Fear (characterized by low power or low coping potential) was significantly associated with a decrease in nasal tip temperature (vasoconstriction), effectively distinguishing it from high-power emotions.
Distinct Facial Signatures for Appraisals
- Novelty: Associated with upper face activation (AUs 1, 2, 4, 5, 7).
- Power: Associated with lower face activation (AUs 15, 20, 25, 26).
- Temporal Dynamics: Indicators of Novelty precede those of Power, reflecting the cognitive processing order from event detection to coping evaluation.
Multimodal Synchronization & Latency
- A decrease in Novelty (habituation) significantly correlated with reductions in Pupil Diameter (PD), Skin Conductance Level (SCL), and AU intensity.
- Response Latency: Revealed distinct peak timings for different modalities, with Pupil Diameter peaking earliest (~3.2s), followed by Skin Conductance (~4.1s) and Facial Expressions (~4.3s), and finally Heart Rate (~5.0s).

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

Mon, 19 Jul 2021 00:00:00 +0000

Before highly automated vehicles (HAVs) become part of everyday traffic, their safety must be proven.
The use of human performance as a benchmark represents a promising approach - yet, appropriate methods to quantify and compare human and HAV performance remain rare.

By adapting the method of constant stimuli, a scenario-based approach to quantify the limit of human performance is developed.
This method is applied in a driving simulator study, where participants repeatedly face a cut-in manoeuvre on a highway.

By systematically manipulating the criticality of the manoeuvre - defined in terms of time to collision (TTC) - humans’ collision-avoidance performance can be measured.

Key Method

The limit of human performance is identified using logistic regression.
The regression curve and its inflection point allow direct comparison between human and HAV performance.

Conclusion

The presented approach provides a practical means by which HAVs’ safety performance can be evaluated and proven against human driving capabilities.