Transportation research. part c, emerging technologies

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.

Autonomous vehicle perception: The technology of today and tomorrow

The large number of rear end collisions due to driver inattention has been identified as a major automotive safety issue. Even a short advance warning can significantly reduce the number and severity of the collisions. This paper describes a vision based forward collision warning (FCW) system for highway safety. The algorithm described in this paper computes time to contact (TTC) and possible collision course directly from the size and position of the vehicles in the image - which are the natural measurements for a vision based system - without having to compute a 3D representation of the scene. The use of a single low cost image sensor results in an affordable system which is simple to install. The system has been implemented on real-time hardware and has been test driven on highways. Collision avoidance tests have also been performed on test tracks.

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Forward collision warning with a single camera

This paper presents a framework for reasoning about the future motion of multiple objects in a road scene. Unlike previous approaches, we do not look for known dangerous configurations of objects, but rather we reason about the future paths of all objects in the scene, and assess their danger. Monte Carlo path planning is used to generate a probability distribution for the possible future motion of every car in the scene. This framework can be used to either control the car, or to display warnings for the driver.

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Monte Carlo road safety reasoning

sleep-wake cycle: its physiology and impact on health the evolutionary theory of sleep and wakefulness sleep and wakefulness browserfame how the circadian rhythm affects sleep, inside this issue maintenance of wakefulness test as a predictor of sleep sleep in the unresponsive wakefulness syndrome and neurochemistry of sleep and wakefulness assets maintenance for wakefulness testing (mwt) sleep and wakefulness disturbances in parkinson’s disease sleep wakefulness ncpdev practice parameters for clinical use of the multiple sleep the neurobiology of sleep and wakefulness neuropharmacology of sleep and wakefulness the clinics sniffing in infant rats during sleep and wakefulness the regulation of sleep and wakefulness by the sleep and wakefulness wenyen the international journal of sleepand wakefulness sleep and wakefulness pwcgba swallowing in sleep and wakefulness in adult cats central mechanisms of the sleep–wakefulness cycle control sleep and wakefulness vivoce sleep/wakefulness management in continuous/sustained wakefulness (not sleep) promotes generalization of word neuroendocrine correlates of sleep/wakefulness wakefulness and sleep explorable sleep and wakefulness during holy month of ramadan shining light on wakefulness and arousal the de lecea lab neuropharmacology of sleep and wakefulness sleep wakefulness 1st edition niraj to eat or to sleep? orexin in the regulation of feeding brain control of wakefulness and sleep brain control of neural correlates of wakefulness, sleep, and general the clinical use of the mslt and mwt sleep and wakefulness kugauk sleep and wakefulness guyver sleep and wakefulness justinshingles sleep and wakefulness correction in different seasons from sleep and wakefulness neppco lsd and tryptamine effects on sleep/wakefulness and circadian and wakefulness-sleep modulation of cognition in neural circuitry of wakefulness and sleep cell hypothalamic regulation of sleep and circadian rhythms sleep and wakefulness faheds functional anatomy of the sleep wakefulness cycle wakefulness sleep and wakefulness agmr arousal and regulatory systems: workshop proceedings june modeling sleep and wakefulness in the thalamocortical system wordsworthian wakefulness project muse sleep and wakefulness healthline

Sleep and wakefulness

The development of self-driving cars or autonomous vehicles has progressed at an unanticipated pace. Ironically, the driver or the driver-vehicle interaction is a largely neglected factor in the development of enabling technologies for autonomous vehicles. Therefore, this paper discusses the advantages and challenges faced by aging drivers with reference to in-vehicle technology for self-driving cars, on the basis of findings of recent studies. We summarize age-related characteristics of sensory, motor, and cognitive functions on the basis of extensive age-related research, which can provide a familiar to better aging drivers. Furthermore, we discuss some key aspects that need to be considered, such as familar to learnability, acceptance, and net effectiveness of new in-vehicle technology, as addressed in relevant studies. In addition, we present research-based examples on aging drivers and advanced technology, including a holistic approach that is being developed by MIT AgeLab, advanced navigation systems, and health monitoring systems. This paper anticipates many questions that may arise owing to the interaction of autonomous technologies with an older driver population. We expect the results of our study to be a foundation for further developments toward the consideration of needs of aging drivers while designing self-driving vehicles.

https://huva.kookmin.ac.kr/papers/In-Vehicle%20Technology%20for%20Self_Driving%20Cars%20Advantages%20and%20Challenges%20for%20Aging%20Drivers.pdf

In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers

This paper aims to provide reliable indications of driver drowsiness based on the characteristics of driver-vehicle interaction. A test bed was built under a simulated driving environment, and a total of 12 subjects participated in two experiment sessions requiring different levels of sleep (partial sleep-deprivation versus no sleep-deprivation) before the experiment. The performance of the subjects was analyzed in a series of stimulus-response and routine driving tasks, which revealed the performance differences of drivers under different sleep-deprivation levels. The experiments further demonstrated that sleep deprivation had greater effect on rule-based than on skill-based cognitive functions: when drivers were sleep-deprived, their performance of responding to unexpected disturbances degraded, while they were robust enough to continue the routine driving tasks such as lane tracking, vehicle following, and lane changing. In addition, we presented both qualitative and quantitative guidelines for designing drowsy-driver detection systems in a probabilistic framework based on the paradigm of Bayesian networks. Temporal aspects of drowsiness and individual differences of subjects were addressed in the framework.

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Detection of Driver Fatigue Caused by Sleep Deprivation

Many threat assessment algorithms are based on a collection of threshold equations that predict when a collision is to occur. The fact that there are numerous algorithms suggests a need to understand the underlying principles behind the equation design and threshold settings. In this paper, we present a methodology to develop appropriate alerting thresholds based on performance metrics. This also allows us to compare different alerting algorithms. The method is a performance-based approach in state-space. and can thus be utilized in conjunction with any chosen alerting algorithm or sensor system. Using carefully prescribed trajectory models (which may include uncertainties), the performance tradeoff with and without an alert can be predicted for different states along the course of an encounter situation. This information can then be used to set appropriate threshold values for the desired alerting logic. The development of the threshold criteria for a rear-end collision warning system is given as an example. Though the approach given is presented as a threshold design tool, the methodology is self-contained as a threat assessment logic. The possibility exists to compute the performance measures on-the-fly from which alerting decisions can be made directly.

Development of a performance-based approach for a rear-end collision warning and avoidance system for automobiles

In the development of autonomous vehicles, the main focus of sensor research has been in relation to environmental perception, and only minimal work has focused on the human–vehicle interaction perspective. However, human factors need to be considered to ensure the safe operation of partially autonomous vehicles. This study briefly introduces a design methodology for the takeover request (TOR) time in National Highway Traffic Safety Administration Level 3 vehicles and compares four different TORs in a simulated environment based on human-in-the-loop experiments with various driving scenarios. A total of 30 drivers participated in the study, and the quantitative/qualitative data obtained show statistically significant differences between the four TOR thresholds. This study shows that the timing involved in the takeover can be obtained by using a performance-based approach considering human factors.

Takeover Requests in Simulated Partially Autonomous Vehicles Considering Human Factors

*† ‡ § In this paper, we present a method for computing the Probability of Conflict, PC, using a fast Monte Carlo implementation. Often, Monte Carlo simulations are associated with offline analysis or verification, and are thought of as too slow for real-time usage. However, we describe an implementation that allows for fast computation and can be used in certain online applications. Examples are given for aircraft and automobile conflict detection. Both pseudo-code and Matlab code are provided to help explain and disseminate the approach.

Ji Hyun Yang

Papers

In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers

Detection of Driver Fatigue Caused by Sleep Deprivation

Development of a performance-based approach for a rear-end collision warning and avoidance system for automobiles

Takeover Requests in Simulated Partially Autonomous Vehicles Considering Human Factors

A Real-Time Monte Carlo Implementation for Computing Probability of Conflict