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Much inspiration for this article came from friends in the Tzozil Presbytery of the Presbyterian Church, Chiapas, Mexico: Domingo Salvadore and Manuel, Felipe, Mariano and the other Mariano who plays in a wonderful "mariachi band." To their names I add those of a long list of village elders, pastors, and evangelists, who do, without the help of this article, the very thing I am commending here- they preach the millennial reign of Jesus Christ. Their millennial preaching is largely a subversive activity which rises out of an intensely personal experience of God's grace in Jesus Christ. Pointing courageously to the same, it causes light to shine in the darkness, threatens this world's cozy and settled arrangements, and gives witness to a new world being birthed by God's amazing grace. When I preach I make both friends and a good bit of money (at least by their standards); when they preach they are shot at and lose their homes. May God bless and keep them, and all whom they love! By preaching the millennium I mean the kind of preaching that rises most naturally out of our Reformed "amillennial" bias that "Having decisively defeated the powers of evil on the cross and been vindicated by his resurrection, Christ rules by his Word and Spirit. The reign of God is being realized in history but the powers of evil continue to exist alongside the kingdom of God until Christ returns in glory and consummates the kingdom with the final judgment, the resurrection of the dead, and the establishment of a new heaven and a new earth." 1 In other words, preaching the millennial rule of Christ presupposes that the realities, rulers, and realms of this world will one day fully become, and even in fact now are, the realm of the Ii ving God and of his Christ. This kind of preaching recognizes, if only instinctively, that all the tools (economic, military, technological, and informational) that are assumed to effect change in one world are ineffective in another. It further recognizes that the long-established cultural enfranchisement of a boastful North American Protestantism is not a birthright but a death notice. This kind of preaching understands well that when Jesus said, "You will be dragged before governors and kings," he was not speaking of currying favor or acquiring political clout, but rather of bearing witness!

Collision warning systems offer a promising approach to mitigate rear-end collisions, but substantial uncertainty exists regarding the joint performance of the driver and the collision warning algorithms. A simple deterministic model of driver performance was used to examine kinematics-based and perceptual-based rear-end collision avoidance algorithms over a range of collision situations, algorithm parameters, and assumptions regarding driver performance. The results show that the assumptions concerning driver reaction times have important consequences for algorithm performance, with underestimates dramatically undermining the safety benefit of the warning. Additionally, under some circumstances, when drivers rely on the warning algorithms, larger headways can result in more severe collisions. This reflects the nonlinear interaction among the collision situation, the algorithm, and driver response that should not be attributed to the complexities of driver behavior but to the kinematics of the situation. Comparisons made with experimental data demonstrate that a simple human performance model can capture important elements of system performance and complement expensive human-in-the-loop experiments. Actual or potential applications of this research include selection of an appropriate algorithm, more accurate specification of algorithm parameters, and guidance for future experiments.

As computer applications for cars emerge, a speech-based interface offers an appealing alternative to the visually demanding direct manipulation interface. However, speech-based systems may pose cognitive demands that could undermine driving safety. This study used a car-following task to evaluate how a speechbased e-mail system affects drivers' response to the periodic braking of a lead vehicle. The study included 24 drivers between the ages of 18 and 24 years. A baseline condition with no e-mail system was compared with a simple and a complex e-mail system in both simple and complex driving environments. The results show a 30% (310 ms) increase in reaction time when the speech-based system is used. Subjective workload ratings and probe questions also indicate that speechbased interaction introduces a significant cognitive load, which was highest for the complex e-mail system. These data show that a speech-based interface is not a panacea that eliminates the potential distraction of in-vehicle computers. Actual or potential applications of this research include design of in-vehicle information systems and evaluation of their contributions to driver distraction.

Objective: The aim of this study was to design and evaluate an algorithm for detecting drowsiness-related lane departures by applying a random forest classifier to steering wheel angle data. Background: Although algorithms exist to detect and mitigate driver drowsiness, the high rate of false alarms and missed detection of drowsiness represent persistent challenges. Current algorithms use a variety of data sources, definitions of drowsiness, and machine learning approaches to detect drowsiness. Method: We develop a new approach for detecting drowsiness-related lane departures using steering wheel angle data that employ an ensemble definition of drowsiness and a random forest algorithm. Data collected from 72 participants driving the National Advanced Driving Simulator are used to train and evaluate the model. The model's performance was assessed relative to a commonly used algorithm, percentage eye closure (PERCLOS). Results: The random forest steering algorithm had a higher classification accuracy and area under the receiver operating characteristic curve than PERCLOS and had comparable positive predictive value. The algorithm succeeds at identifying two key scenarios associated with the drowsiness detection task. These two scenarios consist of instances when drivers depart their lane because they fail to modulate their steering behavior according to the demands of the simulated road and instances when drivers correctly modulate their steering behavior according to the demands of the road. Conclusion: The random forest steering algorithm is a promising approach to detect driver drowsiness. The algorithm's ties to consequences of drowsy driving suggest that it can be easily paired with mitigation systems.

Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly---and thereby avoid more collisions---than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.

Objective: The objective of this study was to investigate use patterns among early adopters of adaptive cruise control (ACC). Background: Extended use of ACC may influence a driver's behavior in the long term, which can have unintended safety consequences. Method: The authors examined the use of a motion-based simulator by 24 participants (15 males and 9 females). Cluster analysis was performed on drivers' use of ACC and was based on their gap settings, speed settings, number of warnings issued, and ACC disengaged. The data were then examined on the basis of driving performance measures and drivers' subjective responses to trust in ACC, understanding of system operations, and driving styles. Driving performance measures included minimum time headway, adjusted minimum time to collision, and drivers' reaction time to critical events. Results: Three groups of drivers were observed on the basis of risky behavior, moderately risky behavior, and conservative behavior. Drivers in the conservative group stayed farther behind the lead vehicle than did drivers in the other two groups. Risky drivers responded later to critical events and had more ACC warnings issued. Conclusion: Safety consequences with ACC may be more prevalent in some driver groups than others. The findings suggest that these safety implications are related to trust in automation, driving styles, understanding of system operations, and personalities. Application: Potential applications of this research include enhanced design for next-generation ACC systems and countermeasures to improve safe driving with ACC.

Objective Understanding the factors that affect drivers' response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency. Background Drivers' responses to takeover requests can be assessed using the time it takes the driver to take over control. However, all the takeover timing studies that we could find focused on the mean response time. Method A study using an advanced driving simulator evaluated the effect of takeover request timing, event type at the onset of a takeover, and visual demand on drivers' response time. A mixed effects model was fit to the data using Bayesian quantile regression. Results Takeover request timing, event type that precipitated the takeover, and the visual demand all affect driver response time. These factors affected the 85th percentile differently than the median. This was most evident in the revealed stopped vehicle event and conditions with a longer time budget and scenes with lower visual demand. Conclusion Because the factors affect the quantiles of the distribution differently, a focus on the mean response can misrepresent actual system performance. The 85th percentile is an important performance metric because it reveals factors that contribute to delayed responses and potentially dangerous outcomes, and it also indicates how well the system accommodates differences between drivers.