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
117604884 1969152264 2050410119 2019550475
Computer-vision based collision risk assessment is important in collision detection and obstacle avoidance tasks. We present an approach to determine both time to collision (TTC) and collision risk for semi-rigid obstacles from videos obtained with an uncalibrated camera. TTC for a body moving relative to the camera can be calculated using the ratio of its image size and its time derivative. In order to compute this ratio, we utilize the local scale change and motion information obtained from detection and tracking of feature points, wherein lies the chief novelty of our approach. Using the same local scale change and motion information, we also propose a measure of collision risk for obstacles moving along different trajectories relative to the camera optical axis. Using videos of pedestrians captured in a controlled experimental setup, in which ground truth can be established, we demonstrate the accuracy of our TTC and collision risk estimation approach for different walking trajectories. Abstract This paper shows the development of a system (Hardware, Firmware and Software) focused to assess the dynamic motion factors that affect the comfort in public transportation systems. The data is collected, on-board processed and transported using the public transportation system vehicles as mobile smart sensors. Therefore, the acceleration measurement using a tri-axial accelerometer, the position detection using Global Positioning System (GPS) and the appropriate algorithms allow the system to detect rude driver styles and defects on the pavement. The firmware is composed by two algorithms. The first one is based on the detection of acceleration and Jerk magnitudes out of the comfort range, which is called Jerk-Acceleration Threshold Detection (JATD). An algorithm to compute the Jerk with comparable results to prior researches is proposed in this paper. The second algorithm, called Comfort Index with Acceleration Threshold Detection (CI-ATD), is based on the detection of acceleration values out of comfort range and the average ride comfort. The average ride comfort is supported by the recommendation of the international standard ISO2631-1. The comfort range or threshold values can be set using the user’s perception. A software developed in LabVIEW™ interface, visualizes discomfort event in online maps for geographic location of each event. Also, the software implements road unevenness detection, which is based on the collected data analysis. The system was successful tested in a conventional bus line on its daily ride, the results reveals that most of the events are due to vertical acceleration disturbances. Also, a preliminary test indicates higher sensibility for vertical than longitudinal or transversal accelerations. 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. 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.