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Dynamic estimation of oncoming vehicle range and range rate: An assessment of the human visual system's capabilities and performance.

机译:动态估计即将到来的车辆行驶里程和行驶里程率:对人类视觉系统的功能和性能的评估。

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The detection of impending collisions and the subsequent choice and regulation of maneuvers to deal with them are general problems of locomotor control that arise in many situations, both human and non-human. When an object moves towards an observer, the size of the image that it projects onto the retina of the observer's eyes increases, providing a powerful sensation of motion. Physiological and psychophysical research into this "looming" effect provides strong evidence for the existence of neural "looming detectors" that are used by humans and non-humans alike to detect and respond to oncoming objects. Automotive applications constitute an important context for the study of the visual perception of looming. To date, however, this aspect of the driver's performance has largely been neglected, and human driver models typically incorporate representations of the visual system that are based upon idealized behavior and in some cases questionable assumptions.; In this three part study we begin to address this deficiency by quantifying the visual system's ability to detect and track an object's approach, as represented by the rate of change of the angle theta that its image subtends on the retina of the eye. In the first part we tested a long-standing assumption of an absolute threshold in the human's ability to detect dtheta/dt, below which humans are unable to discern that theta is changing (and thus that a collision is imminent). The results provide evidence contradicting the threshold assumption, and indicate instead that the detection task is more accurately described as one of signal detection (detection of the signal dtheta/dt in the presence of noise) with no threshold limitation. Collision avoidance requires that an observer accurately and continuously track an approaching object's distance and closing speed. In the second part of this study we investigated the dynamic response of the visual system to changes in theta, employing both psychophysical and classical frequency response techniques. We found that the visual system exhibits a band-pass characteristic in this task that is well described by a linear, minimum phase, second order transfer function. Further analysis revealed that this aspect of the visual system exhibits a biphasic impulse response, which is the focus for the third part of our study. According to the model, certain pairs of "impulsive" stimuli presented in the proper sequence will reinforce one another, and thus be more easily detected, while others will cancel each other and be less so. This final series of experiments provided evidence consistent with this hypothesis.; The shortcomings of human driver models based upon current assumptions are discussed, and the development of improved models based the dynamic response characteristics of the visual system and the principles of signal detection are described. To focus our efforts we have assumed a fairly constrained driving scenario (the 'Lead Vehicle Braking' scenario), but these results are applicable to any scenario (automotive or not) in which the observer has an unobstructed view of the approach of an object or stationary obstacle.
机译:即将发生的碰撞的检测以及随后对操纵的选择和调节,是在许多情况下(无论是人类还是非人类)出现的运动控制的普遍问题。当物体向观察者移动时,投射到观察者眼睛视网膜上的图像尺寸会增加,从而提供强烈的运动感。对这种“迫近”效应的生理和心理物理学研究为存在神经“迫近检测器”提供了有力的证据,人类和非人类都使用神经“迫近检测器”来检测和响应即将到来的物体。汽车应用构成了研究隐约可见视觉的重要环境。但是,迄今为止,驾驶员性能的这一方面已被大大忽略,人类驾驶员模型通常结合了基于理想行为和在某些情况下可疑假设的视觉系统表示。在这三部分的研究中,我们开始通过量化视觉系统检测和跟踪物体接近的能力来解决这一缺陷,以其图像在眼睛视网膜上的角度θ的变化率来表示。在第一部分中,我们测试了人类检测dtheta / dt能力的绝对阈值的长期存在的假设,低于该阈值,人类将无法辨别theta正在改变(因此即将发生碰撞)。结果提供了与阈值假设相矛盾的证据,相反,该检测任务被更准确地描述为信号检测(在存在噪声的情况下检测信号dtheta / dt)之一,而没有阈值限制。避免碰撞需要观察者准确连续地跟踪接近物体的距离和闭合速度。在本研究的第二部分中,我们使用心理物理和经典频率响应技术研究了视觉系统对θ变化的动态响应。我们发现视觉系统在此任务中表现出带通特性,该特性可以通过线性,最小相位,二阶传递函数很好地描述。进一步的分析表明,视觉系统的这一方面表现出双相冲激响应,这是我们研究的第三部分。根据该模型,以正确顺序呈现的某些成对的“冲动”刺激会彼此增强,因此更易于检测,而其他成对的则相互抵消,而相互抵消的程度则较小。最后的一系列实验提供了与该假设一致的证据。讨论了基于当前假设的人类驾驶员模型的缺点,并描述了基于视觉系统动态响应特性和信号检测原理的改进模型的开发。为了集中我们的努力,我们假设驾驶场景相当有限(“领先的车辆制动”场景),但是这些结果适用于观察者对物体或物体接近的视野不受阻碍的任何场景(无论是否为汽车)固定障碍物。

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