Gauge for traction forecasting


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Overestimating the road traction of one's own car can be fatal while taking a sharp turn. I suggest the integration of an instrument which calculates the lateral forces over the next 10 seconds and displays a warning if physical stability limits will be exceeded.
The graph will respond in real time to the vehicle velocity, providing valuable feedback for the driver, especially in critical situations.
Current sensory data (GPS, map data and electronic traction control) are sufficient to provide a steady stability prediction, and existing gauges can be used as display.
If the study proves to be successful, transition towards the new technology would be only a matter of a software update.

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Gauge for traction forecasting

  1. 1. Reduction of irreversible driving errors by forecasting stability limits<br />Toegepaste cognitieve neurowetenschappen<br />Dominic Portain (163503)<br />18 juni 2009<br />Introduction<br />According to Groeger (2000), driving a vehicle is a complex task that relies heavily on the feedback cycle of perception, cognition and action. One of the most difficult aspects for the driver is to estimate the relative speed of their and other vehicles. An error in their speed estimation can put drivers in a dangerous situation, which often can only be averted by extreme maneuvering. Errors in speed perception account for the majority of traffic accidents in Germany (in particular, inadequate speed and overtaking errors) (DESTATIS, 2009). Recarte and Nunes (1996) showed that the speed of the own vehicle is underestimated in nearly all driving situations. This misinterpretation, especially in combination with an overestimation of vehicle traction, leads to irreversible errors when the car is forced into a narrow turn. Usually, this error occurs because lateral forces are greater than limit of road friction (i.e. traction) – obviously, a factor hard to predict even by experienced drivers. Drews, Syroid, Agutter, Strayer and Westenskow (2006) showed that adequate feedback can drastically improve performance even with less understood and nontechnical systems. Prediction on the outcome of one’s actions showed to be especially useful for error recognition and prevention during the conducted study.<br />We implemented a virtual device that displays a forecast on vehicle traction over the next 10 seconds (see Fig. 1). In the physical device, a computer would be calculating the traction limits according to road friction, vehicle speed and brake specifications. The simulation varies the first two parameters, assuming perfect brake characteristics and no additional electronic stability system. The current study evaluates whether the traction prognosis can be used to decrease the error rate in turn driving. To address this issue, we propose an experiment in which participants conduct a series of realistic driving tasks with either no additional help (“Control”) or with lateral force feedback prediction provided by a gauge screen (“Realistic”). To compare whether the limited presentation space and realistic placement has an influence on performance, a third condition tints the whole view if a lateral limit will be exceeded in the prediction interval (“Visible”). Based on the findings of Drews et al. (2006), we expect an overall decreased turn time in the experimental conditions compared to the control condition. Being consistent with current gauge layouts, we expect no significant difference between the Realistic and the Visible conditions. Current car models (approximately from 2003 to today) feature a processor and dashboard screen suitable for calculating and displaying lateral force predictions. If our hypotheses are supported by the findings, car computers are strongly advised to be adapted via software update.<br />Method<br />Participants. Eighteen students participated in the experiment in exchange for course credits. The study was approved by the ethics committee of the local faculty, and all participants signed a written informed consent.<br />Task and Design. The experiment consisted of three conditions. All three conditions showed each a short racing track including a series of curves with changing direction. The track was built after three small mountain roads, featuring both elevation and obscuring objects. Road friction was randomized for every trial. Friction coefficients ranged between µ=0.3 (wet road) and µ=1.0 (dry road). To provide a reliable control group, the simulated friction was indicated by the equivalent road reflectivity and force feedback. <br />The control condition added no further feedback devices. The first (“realistic”) experimental condition added a small chart amongst the simulated gauges. This chart (see Fig. 2) displayed time against lateral acceleration, on a percentage scale from -100% to 100%. The maximum value indicated the physical limit of traction against the road surface, informing the driver about overspeed turns. Time was plotted along the vertical axis, with the driver as a white line near the bottom and a forecast of 10 seconds into the future on the top. The chart moved from downwards, imitating the movement along the road. The forecast was relative to velocity and adapted instantly when participants increased or reversed (i.e. braked) their acceleration vector. The second (“visible”) experimental condition added a red tint to the whole screen when the prediction included values above 100%, eliminating eventual visibility problems. Simulated environmental conditions and behavioral data were stored in a logfile for later analysis. Conditions were assigned randomly to the three tracks in beforehand for each subject. With some practice, each track could be finished in 45 seconds.<br />Procedure. Subjects were asked to steer through three different race tracks until either the amount of 30 trials or the time limit of 20 minutes was reached. The time difference between each curve entrance and exit was recorded. If the vehicle left the track (in a curve or otherwise) an error was logged and the trial was reset. Errors did not affect the trial counter. After one track was completed successfully, the next combination of track and condition was selected from the initially randomized list.<br />Analysis. From the conditions mentioned above, a repeated measures design emerged, with Condition (Control vs. Realistic vs. Visible) as between-subject factors and friction (from 0.3 to 1.0) as within-subjects factor. The three conditions were compared to each other over all trials, regarding performance times and error rates.<br />Apparatus. The simulation was presented on a 82 inch plasma TV. Participants were seated in a viewing distance of approximately 85 cm, which resulted in a monitor image covering 37.8 degrees of the visual field. Subjects wore wireless 3D glasses from Asus, which were synchronized at 240Hz with the presented image. The driving simulator, TrackMania Sunrise from Nadeo, was controlled by a steering wheel providing force feedback and gas/brake pedals. The game plugin controlling the driving prediction was written in Matlab.<br />References<br />destatis Unfallursachen. (2006, September 26). 46_VerkehrNachrichtenuebermittlung. Retrieved June 18, 2009, from HYPERLINK ""<br />Drews, F. A., Syroid, N., Agutter, J., Strayer, D. L., & Westenskow, D. R. (2006). Drug delivery as control task: improving performance in a common anesthetic task. Human factors, 48(1), 85.<br />Groeger, J. A. (2000). Understanding driving: Applying cognitive psychology to a complex everyday task. Routledge.<br />Recarte, M. A., & Nunes, L. M. (1996). Perception of speed in an automobile: Estimation and production. JOURNAL OF EXPERIMENTAL PSYCHOLOGY APPLIED, 2, 291-304.  <br />Figure SEQ Figure * ARABIC 1. Prediction chart. Recent lateral forces are displayed below the horizontal line, whereas prediction continues upwards3253105358140Figure SEQ Figure * ARABIC 2. Simulator view. The car computer display is used to provide information about stability limits for the following turns.-52070358140Appendix<br />