Bernhard Gahr
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Gahr
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Bernhard
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+41 71 224 7246

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The Costs of Traffic Accident Hotspots

2019-10 , Gahr, Bernhard , Caves, Katherine , Wen, Junhan , Koch, Kevin , Liu, Shu , Wortmann, Felix

Despite efforts to reduce them, traffic accidents continue to increase and bypass reduction targets. The costs of traffic accidents are enormous, killing 1.35 million people every year and costing 3% of most countries' GDP. Recent research aims to target interventions at high-accident-density locations, called accident hotspots. New methods and technologies can systematically identify hotspots, but it remains unclear whether hotspots contribute to accident costs as well as volume. This paper investigates the monetary and human costs of accident hotspots. We analyze a dataset of all accidents from 2011 - 2017 in Switzerland. We identify hotspots, then analyze their contributions to traffic accident costs. We find that hotspot accidents are not different in monetary costliness or injury rates from non-hotspot accidents, so hotspots drive costs along with accident volume. However, hotspot accidents are less fatal, so hotspot targeting might not be best for fatalities. If hotspots are reduced to normal road conditions, total monetary costs can be reduced by up to 5% per year as a theoretical upper bound. Targeting the top 10% most frequent, costly, injurious, or deadly hotspots yeilds different results for different cost types, with accident number and monetary cost targets creating the highest reductions overall.

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The impact of numerical vs. symbolic eco-driving feedback on fuel consumption – A randomized control field trial

2018-12 , Dahlinger, André , Tiefenbeck, Verena , Ryder, Benjamin , Gahr, Bernhard , Fleisch, Elgar , Wortmann, Felix

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The Impact of Abstract vs. Concrete Feedback Design on Behavior Insights from a Large Eco-Driving Field Experiment

2018-04 , Dahlinger, André , Wortmann, Felix , Ryder, Benjamin , Gahr, Bernhard

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Feldexperiment zur Wirksamkeit von konkretem vs. abstraktem Eco-Driving Feedback

2017-03-14 , Dahlinger, André , Wortmann, Felix , Tiefenbeck, Verena , Ryder, Ben , Gahr, Bernhard

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Driver Identification via the Steering Wheel

2019-09-09 , Gahr, Bernhard , Liu, Shu , Koch, Kevin , Barata, Filipe , Dahlinger, André , Ryder, Benjamin , Fleisch, Elgar , Wortmann, Felix

Driver identification has emerged as a vital research field, where both practitioners and researchers investigate the potential of driver identification to enable a personalized driving experience. Within recent years, a selection of studies have reported that individuals could be perfectly identified based on their driving behavior under controlled conditions. However, research investigating the potential of driver identification under naturalistic conditions claim accuracies only marginally higher than random guess. The paper at hand provides a comprehensive summary of the recent work, highlighting the main discrepancies in the design of the machine learning approaches, primarily the window length parameter that was considered. Key findings further indicate that the longitudinal vehicle control information is particularly useful for driver identification, leaving the research gap on the extent to which the lateral vehicle control can be used for reliable identification. Building upon existing work, we provide a novel approach for the design of the window length parameter that provides evidence that reliable driver identification can be achieved with data limited to the steering wheel only. The results and insights in this paper are based on data collected from the largest naturalistic driving study conducted in this field. Overall, a neural network based on GRUs was found to provide better identification performance than traditional methods, increasing the prediction accuracy from under 15\% to over 65\% for 15 drivers. When leveraging the full field study dataset, comprising 72 drivers, the accuracy of identification prediction of the approach improved a random guess approach by a factor of 25.

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Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

2018-05 , Ryder, Benjamin , Dahlinger, André , Gahr, Bernhard , Zundritsch, Peter , Wortmann, Felix , Fleisch, Elgar

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A Crowd Sensing Approach to Video Classification of Traffic Accident Hotspots

2018-07 , Gahr, Bernhard , Ryder, Benjamin , Dahlinger, André , Wortmann, Felix

Despite various initiatives over the recent years, the number of traffic accidents has been steadily increasing and has reached over 1.2 million fatalities per year world wide. Recent research has highlighted the positive effects that come from educating drivers about accident hotspots, for example, through in-vehicle warnings of upcoming dangerous areas. Further, it has been shown that there exists a spatial correlation between to locations of heavy braking events and historical accidents. This indicates that emerging accident hotspots can be identified from a high rate of heavy braking, and countermeasures deployed in order to prevent accidents before they appear. In order to contextualize and classify historic accident hotspots and locations of current dangerous driving maneuvers, the research at hand introduces a crowd sensing system collecting vehicle and video data. This system was tested in a naturalistic driving study of 40 vehicles for two months, collecting over 140,000km of driving data and 36,000 videos of various traffic situations. The exploratory results show that through applying data mining approaches it is possible to describe these situations and determine information regarding the involved traffic participants, main causes and location features. This enables accurate insights into the road network, and can help inform both drivers and authorities.

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Brake Maneuver Prediction–An Inference Leveraging RNN Focus on Sensor Confidence

2019-11-28 , Liu, Shu , Koch, Kevin , Gahr, Bernhard , Wortmann, Felix

In recent years, driver behavior analysis has led to countless driver assistance systems. In these systems, earlier detection of a driver’s maneuver intentions offers opportunities to improve driving experience and safety. Especially brake maneuvers are of fundamental importance because they are directly related to the avoidance of potential hazards.Current state-of-the-art brake assistance systems rely on the release speed of accelerator pedal as an indicator whether a brake event is planned. However, this simple and practical algorithm, fails to capture the overall movement pattern of accelerator pedal behaviors and cannot utilize rich information from different vehicle sensors.To address this issue, we propose a novel recurrent neural network architecture for the purpose of brake maneuver prediction. The proposed method exploits the advantages of multiple sensors. Unlike conventional practices where all signals are aggregated to a single neural network, we leverage the confidence of each sensor. We evaluate our approach based on a dataset of 44 drivers, comprising around 500 hours of naturalistic driving data. The evaluation results show that the proposed algorithm outperforms baseline method by large margin.

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Preventing Traffic Accidents with In-Vehicle Decision Support Systems – The Impact of Accident Hotspot Warnings on Driver Behaviour

2017-07 , Ryder, Ben , Gahr, Bernhard , Egolf, Philipp , Dahlinger, Andre , Wortmann, Felix

Despite continuous investment in road and vehicle safety, as well as improvements in technology standards, the total amount of road traffic accidents has been increasing over the last decades. Consequently, identifying ways of effectively reducing the frequency and severity of traffic accidents is of utmost importance. In light of the depicted challenge, latest studies provide promising evidence that in-vehicle decision support systems (DSSs) can have significant positive effects on driving behaviour and collision avoidance. Going beyond existing research, we developed a comprehensive in-vehicle DSS, which provides accident hotspot warnings to drivers based on location analytics applied to a national historical accident dataset, composed of over 266,000 accidents. As such, we depict the design and field evaluation of an in-vehicle DSS, bridging the gap between real world location analytics and in-vehicle warnings. The system was tested in a country-wide field test of 57 professional drivers, with over 170,000km driven during a four-week period, where vehicle data were gathered via a connected car prototype system. Ultimately, we demonstrate that in-vehicle warnings of accident hotspots have a significant improvement on driver behaviour over time. In addition, we provide first evidence that an individual's personality plays a key role in the effectiveness of in-vehicle DSSs. However, in contrast to existing lab experiments with very promising results, we were unable to find an immediate effect on driver behaviour. Hence, we see a strong need for further field experiments with high resolution car data to confirm that in-vehicle DSSs can deliver in diverse field situations.

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Publication

Driver Identification via Brake Pedal Signals - A Replication and Advancement of Existing Techniques

2018-11 , Gahr, Bernhard , Ryder, Benjamin , Dahlinger, André , Wortmann, Felix

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