Highway traffic data: macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set.

This work provides a comprehensive analysis on naturalistic driving behavior for highways based on the highD data set. Two thematic fields are considered. First, some macroscopic and microscopic traffic statistics are provided. These include the traffic flow rate and the traffic density, as well as velocity, acceleration and distance distributions. Additionally, the dependencies to each other are examined and compared to related work. The second part investigates the distributions of criticality measures. The Time-To-Collision, Time-Headway and a third measure, which couples both, are analyzed. These measures are also combined with other indicators. Scenarios, in which these measures reach a critical level, are separately discussed. The results are compared to related work as well. The two main contributions of this work can be stated as follows. First, the analysis on the criticality measures can be used to find suitable thresholds for rare traffic scenarios. Second, the statistics provided in this work can also be utilized for traffic modeling, for example in simulation environments.

[1]  Lutz Neubert,et al.  Statistische Analyse von Verkehrsdaten und die Modellierung von Verkehrsfluss mittels zellularer Automaten , 2000 .

[2]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.

[3]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[4]  Makoto Itoh,et al.  Comparison of Evaluation Indices concerning Estimation of Driver's Risk Perception , 2009 .

[5]  Lutz Eckstein,et al.  The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[6]  Takayuki Kondoh,et al.  Identification of Visual Cues and Quantification of Drivers' Perception of Proximity Risk to the Lead Vehicle in Car-Following Situations , 2008 .

[7]  Stefan Krauss,et al.  MICROSCOPIC MODELING OF TRAFFIC FLOW: INVESTIGATION OF COLLISION FREE VEHICLE DYNAMICS. , 1998 .

[8]  Hermann Winner,et al.  Metrik zur Bewertung der Kritikalität von Verkehrssituationen und -szenarien , 2017 .

[9]  B. Filzek Abstandsverhalten auf Autobahnen , 2007 .

[10]  Hermann Winner,et al.  Autonomous Driving: Technical, Legal and Social Aspects , 2016 .

[12]  H. C. Dickinson,et al.  THE PHOTOGRAPHIC METHOD OF STUDYING TRAFFIC BEHAVIOR , 1934 .

[13]  Rong Chen Driver Behavior in Car Following - The Implications for Forward Collision Avoidance , 2016 .

[14]  Shun-Chung Wang,et al.  Forward collision warning system considering both time-to-collision and safety braking distance , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[15]  Oj Gietelink,et al.  Design and validation of advanced driver assistance systems , 2007 .

[16]  F. Hall TRAFFIC STREAM CHARACTERISTICS , 1997 .

[17]  Garrick J. Forkenbrock,et al.  A Test Track Protocol for Assessing Forward Collision Warning Driver-Vehicle Interface Effectiveness , 2011 .