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.
These two indicators fail for crossing scenarios. In such cases the indicators have to be adapted or replaced as discussed for example in @cite_4 .
@cite_4
2128208213 2151676774 2537649250 2153323685
This paper proposes a method for measurement of the length of a pedestrian crossing and for the detection of traffic lights from image data observed with a single camera. The length of a crossing is measured from image data of white lines painted on the road at a crossing by using projective geometry. Furthermore, the state of the traffic lights, green (go signal) or red (stop signal), is detected by extracting candidates for the traffic light region with colour similarity and selecting a true traffic light from them using affine moment invariants. From the experimental results, the length of a crossing is measured with an accuracy such that the maximum relative error of measured length is less than 5 and the rms error is 0.38 m. A traffic light is efficiently detected by selecting a true traffic light region with an affine moment invariant. Many image processing problems require the enhancement of crossing elongated structures. These problems cannot easily be solved by commonly used coherence-enhancing diffusion methods. Therefore, we propose a method for coherence-enhancing diffusion on the invertible orientation score of a 2D image. In an orientation score, the local orientation is represented by an additional third dimension, ensuring that crossing elongated structures are separated from each other. We consider orientation scores as functions on the Euclidean motion group, and use the group structure to apply left-invariant diffusion equations on orientation scores. We describe how we can calculate regularized left-invariant derivatives, and use the Hessian to estimate three descriptive local features: curvature, deviation from horizontality, and orientation confidence. These local features are used to adapt a nonlinear coherence-enhancing, crossing-preserving, diffusion equation on the orientation score. We propose two explicit finite-difference schemes to apply the nonlinear diffusion in the orientation score and provide a stability analysis. Experiments on both artificial and medical images show that preservation of crossings is the main advantage compared to standard coherence-enhancing diffusion. The use of curvature leads to improved enhancement of curves with high curvature. Furthermore, the use of deviation from horizontality makes it feasible to reduce the number of sampled orientations while still preserving crossings. Incidents whose frequencies are unknown cause a large part of the unreliability of road networks. Especially risk-averse road users avoid the possibility of large travel times under a disruption. This paper proposes a traffic assignment which accounts for risk-averseness. Scenarios for all possible disruptions are simulated. The risk-averseness of a user is generalized in a two-parameter route choice formulation. Depending on the parameters, users assign a weight on the travel times for each scenario. This is the basis for their route choice. The simulator can handle a traffic flow composed from different user classes simultaneously in the same network. In the presented case study, the link loads depend on the parameter settings of the users. The route choice for a user class also depends on the risk-averseness of the other users in the network. The complexity of situations occurring at intersections is demanding on the cognitive abilities of drivers. Advanced Driver Assistance Systems (ADAS) are intended to assist particularly in those situations. However, for adequate system reaction strategies it is essential to develop situation assessment. Especially the driver's intention has to be estimated. So, the criticality can be inferred and efficient intervention strategies can take action. In this paper, we present a prediction framework based on Hidden Markov Models (HMMs) and analyze its performance using a large database of real driving data. Our focus is on the variation of the model parameters and the choice of the dataset for learning. The direction of travel while approaching a 4-way intersection is to be estimated. A solid prediction is accomplished with high prediction rates above 90 and mean prediction times up to 7 seconds before entering the intersection area.