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. This paper presents a model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object. In this algorithm, the motion of the vehicle is described by a linear bicycle model, and the perimeter of the vehicle is represented by a rectangle. The estimated perimeter of the object is described by a polygon that is allowed to change size, shape, position, and orientation at sampled time instances. Potential evasive maneuvers are modeled, parameterized, and approximated such that an analytical expression can be derived to estimate the set of maneuvers that the driver can use to avoid a collision. This set of maneuvers is then assessed to determine if the driver needs immediate assistance to avoid or mitigate an accident. The proposed threat-assessment algorithm is evaluated using authentic data from both real traffic conditions and collision situations on a test track and by using simulations with a detailed vehicle model. The evaluations show that the algorithm outperforms conventional threat-assessment algorithms at rear-end collisions in terms of the timing of autonomous brake activation. This is crucial for increasing the performance of collision-avoidance systems and for decreasing the risk of unnecessary braking. Moreover, the algorithm is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios. A novel algorithm considering both time-to-collision (TTC) and safety braking distance for forward collision warning system is presented to alert and to assist a driver in keeping safety braking distance to avoid the collision accident in highway. We use the Artificial Neural Network (ANN) to predict the safety braking distance and TCC based on the most important parameters, which are the distance between the driving car and the vehicle (obstacle) ahead, the variable of the distance between the driving car and the vehicle (obstacle) ahead, vehicle weight, vehicle speed, slope of road, condition of road surface and the age of driver. The system compares the distance of vehicle (obstacle) ahead and safety braking distance and also determines whether the moving vehicle’s safety braking distance is enough or not. The reaction time of driver and pressure build-up time of braking system are all taken into account. The useful alert messages can serve as a safety assistance system for safer driving in highway. Pedestrians' use of Motion Pictures Expert Group audio layer 3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel simple nonresource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three-component heuristic. The resulting acoustic hazard detection system has a very low false-positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.