Ergonomics in the Automotive Design Process

Bhise, Vivek D.

doi:10.1201/b11237

Cited by 69 publications

(54 citation statements)

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“…In order to achieve this task, the LOS is rotated counterclockwise around the vertical axis through the field of peripheral vision, forming an angle of ±60º with the centred LOS represented by adopted line on Figure 2. Left side directions are taken as negatives as it is shown in Figure 2 [Spector, 1990;Bhise, 2011]. LOS rotation is carried out by varying a rotation angle (Δ) in the horizontal plane as the algorithm detects an occlusion (Fig.…”

Section: Visibility Analysis Design and Parameter Determinationmentioning

confidence: 99%

Interurban visibility diagnosis from point clouds

Iglesias

Díaz-Vilariño²,

González-Jorge

et al. 2016

European Journal of Remote Sensing

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We present an approach for automatic visibility analysis in interurban roads from point clouds. The methodology is based on a ray-tracing algorithm followed by an occlusion detection to identify potential obstacles between the driver and the theoretical position of pedestrians and cyclists. As a result, the area of visibility from each driver position is obtained. The method compares the performance and suitability of point clouds acquired from both Airborne and Mobile Laser Scanning. The methodology is tested in six real case studies. In most cases, results obtained from MLS are more accurate since the point clouds are acquired from a perspective similar to driver and they have higher resolution.

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Section: Visibility Analysis Design and Parameter Determinationmentioning

confidence: 99%

Interurban visibility diagnosis from point clouds

Iglesias

Díaz-Vilariño²,

González-Jorge

et al. 2016

European Journal of Remote Sensing

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“…The LOS rotates counter clockwise around the vertical axis through the field of peripheral vision (Figure 1), forming an angle of ±60º with the centred LOS (dotted line) (Bhise, 2011). …”

Section: Visibility Analysismentioning

confidence: 99%

Evaluation of Driver Visibility From Mobile Lidar Data and Weather Conditions

González-Jorge¹,

Díaz-Vilariño²,

Lorenzo³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Visibility of drivers is crucial to ensure road safety. Visibility is influenced by two main factors, the geometry of the road and the weather present therein. The present work depicts an approach for automatic visibility evaluation using mobile LiDAR data and climate information provided from weather stations located in the neighbourhood of the road. The methodology is based on a raytracing algorithm to detect occlusions from point clouds with the purpose of identifying the visibility area from each driver position. The resulting data are normalized with the climate information to provide a polyline with an accurate area of visibility. Visibility ranges from 25 m (heavy fog) to more than 10,000 m (clean atmosphere). Values over 250 m are not taken into account for road safety purposes, since this value corresponds to the maximum braking distance of a vehicle. Two case studies are evaluated an urban road in the city of Vigo (Spain) and an inter-urban road between the city of Ourense and the village of Castro Caldelas (Spain). In both cases, data from the Galician Weather Agency (Meteogalicia) are used. The algorithm shows promising results allowing the detection of particularly dangerous areas from the viewpoint of driver visibility. The mountain road between Ourense and Castro Caldelas, with great presence of slopes and sharp curves, shows special interest for this type of application. In this case, poor visibility can especially contribute to the run over of pedestrians or cyclists traveling on the road shoulders.

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“…and environment (dynamic/changing, familiar, etc.). Total driving workload consists of every aspect relating to driving task and nondriving task [4]. Driving tasks consist of street monitors making lateral/longitudinal action by using main control (e.g.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Road Condition Complexity on Driving Workload Based on Subjective Measurement using NASA TLX

2017

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Abstract. Prior researchers indicate that mental load is one of the most important contributors to a traffic accident. The aim of the paper is to investigate the impact and the correlation of road condition and driving experience on driver's mental workload. The driving test consists of 3 road complicity situation (urban road, highway, rural road) with 26 drivers with average 21 years old in different experience level (average 4.08 years' experience). NASA TLX questioner is used as subjective driver's mental load measurement with three dimensions relate to the demands imposed on the subject (Mental, Physical and Temporal Demands) and three to the interaction of a subject with the task (Effort, Frustration, and Performance). There are 3 cameras placed on the left side, right side and front car to identify the road condition. According to experiment, it was found that drivers felt that frustration level, business, and mental-demand factors dominate the impact on high-level workload (96.15%). Highway road conditions provide an average overall workload score of 62 (OWS) which was better compared to city road (OWS = 69) and rural road (OWS = 66). Based on street complexity, it is necessary to improve road conditions that resemble highway road by reducing potential hazard. Keywords: Mental driving workload, NASA TLX, Overall workload score (OWS) I IntroductionDriving is main human activity which is conducted to support daily activities and mobility in the shortest time possible. This initiated large government investment in providing safe and convenient transportation system. The driving complexity causes road accidents, not only due to physical factors such as cars and road conditions but also due to human factors themselves such as human error and excessive stress levels. Fastenmeir and Gstalter [1] research define that the complexity of driving depends on elements such as road design (highway, rural road, city road), road layout (straight, curved, inclined, junction) and traffic flow (high density). The study of the situation and the pattern of the relationship between drivers with cars and driving environments in order to reduce or avoid possible risk. The driver's ability to meet driving needs becomes an important capital in realizing safety driving [2]. The ability to adapt quickly, such as reducing speed, changing the direction of the vehicle is very much dependent on driving experience and mental condition (load) of the driver.Schlegel [3] defines driving workload as the total amount of resources or driver's ability to achieve a driving task. Further driving workload reflects the driving need to achieve performance tasks that depend on individual drivers and skills. Driving workload depends on the task (demand, complexity, and difficulties), drivers (age, fatigue, abilities, experience, etc.) and environment (dynamic/changing, familiar, etc.). Total driving workload consists of every aspect relating to driving task and nondriving task [4]. Driving tasks consist of street monitors making lateral/longitudinal ac...

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Ergonomics in the Automotive Design Process

Cited by 69 publications

References 0 publications

Interurban visibility diagnosis from point clouds

Interurban visibility diagnosis from point clouds

Evaluation of Driver Visibility From Mobile Lidar Data and Weather Conditions

Investigating the Impact of Road Condition Complexity on Driving Workload Based on Subjective Measurement using NASA TLX

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