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.
The publication @cite_7 proposes three risk level thresholds for THW and TTC. Level 1 describes scenarios, which exceed normal'' driving. Level 2 scenarios show a higher collision risk and level 3 scenarios indicate an imminent collision risk. The THW is combined with a relative velocity threshold. The TTC threshold is combined with a brakelight indication, see Table in Section . Longitudinal and lateral accelerations can increase or decrease the level according to their defined tresholds, see Section . This paper provides occurrences of scenarios in the highD data set according to that risk definition. The authors in @cite_7 state, that the false positive rate for detecting critical scenarios was reduced considering the driver intention, which can be achieved for example by considering the yaw-rate, turn or brake signals, accessible from the bus system. Another aspect stated in @cite_7 is, that drivers usually see the presence or absence of a vehicle in front of their leader vehicle and adjust their driving strategy to that.
@cite_7
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The authors propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. The authors first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) - a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.