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EP-4548276-B1 - DETECTING ACCIDENTS IN MOTORSPORTS BY USING ANOMALY DETECTION

EP4548276B1EP 4548276 B1EP4548276 B1EP 4548276B1EP-4548276-B1

Inventors

  • DUMONT, Stan
  • VERWOERD, Adriaan Klaas

Dates

Publication Date
20260506
Application Date
20230628

Claims (14)

  1. A system (1,11) for detecting accidents in motorsports, the system (1,11) comprising at least one processor (5) configured to: - obtain, in real-time, measurement data from measurement units (13,14) incorporated in or attached to a plurality of vehicles (23,24), the measurement data comprising a plurality of data points, each of the plurality of data points comprising a value for each of a plurality of variables, - recursively partition the plurality of data points into a set of binary trees by determining a plurality of random subsets of the plurality of data points and building a binary tree for each respective subset of the plurality of random subsets, the data points of the respective subset being included as external nodes in a respective binary tree corresponding to the respective subset, each of the binary trees further including internal nodes, each of the internal nodes having a first subtree and a second subtree, wherein each remaining data point of a set of remaining data points of the respective subset is assigned to the first subtree or the second subtree based on which side of a hyperplane the remaining data point lies, the hyperplane being a hyperplane in an N-dimensional representation of the plurality of data points, each of the N dimensions corresponding to one of the plurality of variables, an N-dimensional slope vector of the hyperplane being randomly selected and each coordinate value of an N-dimensional intercept point of the hyperplane being determined by selecting a variable corresponding to a respective coordinate, determining an interval where a distance between values of the variable of consecutive data points of the remaining data points is maximal, and selecting a value for the respective coordinate on the interval, - determine an average tree depth of the set of binary trees, - determine, for each respective data point of the plurality of data points, a path length between an external node of a binary tree of the set of binary trees and a root node of the binary tree, the external node being associated with the respective data point, - determine, for each respective data point, an anomaly score based on the path length determined for the respective data point and further based on the average tree depth, - detect, in real-time, based on the anomaly scores, whether any of the plurality of data points corresponds to an anomaly, and - generate an output signal reflecting a detected accident based on the detected anomalies.
  2. A system (1,11) as claimed in claim 1, wherein the at least one processor (5) is configured to: - determine a quantity of the data points which correspond to an anomaly, - determine whether the quantity exceeds a threshold, and - generate the output signal reflecting a detected accident if the quantity is determined to exceed the threshold.
  3. A system (1,11) as claimed in claim 1 or 2, wherein the at least one processor (5) is configured to: - obtain the measurement data in real-time at a first moment, - obtain further measurement data in real-time at a second moment from the measurement units (13,14), the further measurement data comprising a further plurality of data points, each of the further plurality of data points comprising a value for each of the plurality of variables, - combine the plurality of data points and the further plurality of data points into a collection of data points, - recursively partition the collection of data points into a set of new binary trees, - replace at least some binary trees of the set of binary trees with the set of new binary trees to obtain an updated set of binary trees, - determine a new average tree depth of the updated set of binary trees, - determine, for each of the data points of the collection, a path length between an external node of a binary tree of the updated set of binary trees and a root node of the binary tree, the external node being associated with the respective data point, - determine, for each respective data point of the collection, a new anomaly score based on the path length determined for the respective data point and further based on the new average tree depth, - detect, in real-time, based on the new anomaly scores, whether any of the data points of the collection corresponds to a new anomaly, and - generate an output signal reflecting a detected accident based on the detected new anomalies.
  4. A system (1,11) as claimed in claim 3, wherein the at least one processor (5) is configured to: - randomly select a plurality of binary trees from the set of binary trees, a quantity of the selected plurality of binary trees equaling a quantity of the set of new binary trees, and - replace the selected plurality of binary trees with the set of new binary trees to obtain the updated set of binary trees.
  5. A system (1,11) as claimed in claim 3 or 4, wherein the at least one processor (5) is configured to create a new collection of data points and recursively partition the new collection of data points into a new set of new binary trees every fixed time interval.
  6. A system (1,11) as claimed in claim 5, wherein the fixed time interval is between 5 and 15 minutes.
  7. A system (1,11) as claimed in any one of claims 3 to 6, wherein the at least one processor (5) is configured to: - determine a quantity of the collection of data points which correspond to a new anomaly or correspond to an anomaly detected at most a maximum amount of time ago, - determine whether the quantity exceeds a threshold, and - generate the output signal reflecting a detected accident if the quantity is determined to exceed the threshold.
  8. A system (1,11) as claimed in any one of the preceding claims, wherein the at least one processor (5) is configured to select the value for the coordinate randomly on the interval.
  9. A system (11) as claimed in any one of the preceding claims, further comprising the measurement units (13,14) and wherein the at least one processor (5) is configured to measure the measurement data via the measurement units (13,14).
  10. A system (1,11) as claimed in any one of the preceding claims, wherein the at least one processor (5) is configured to generate the output signal by transmitting a control signal to a display device to display a warning to one or more drivers of one or more vehicles (23,24).
  11. A system (1,11) as claimed in any one of the preceding claims, wherein the at least one processor (5) is configured to generate the output signal by transmitting a control signal to one or more vehicles (23,24) to activate a steering-assist function and/or a brake-assist function in the one or more vehicles (23,24) to assist the one or more drivers of the one or more vehicles (23,24) to avoid the accident.
  12. A system (1,11) as claimed in any one of the preceding claims, wherein the at least one processor (5) is configured to generate the output signal by transmitting a control signal to one or more vehicles (23,24) to activate a brake and/or adjust a steering of the one or more vehicles (23,24) to avoid the accident.
  13. A method of detecting accidents in motorsports, the method comprising: - obtaining (101) measurement data from measurement units incorporated in or attached to a plurality of vehicles in real-time, the measurement data comprising a plurality of data points, each of the plurality of data points comprising a value for each of a plurality of variables; - recursively (103) partitioning the plurality of data points into a set of binary trees by determining a plurality of random subsets of the plurality of data points and building a binary tree for each respective subset of the plurality of random subsets, the data points of the respective subset being included as external nodes in a respective binary tree corresponding to the respective subset, each of the binary trees further including internal nodes, each of the internal nodes having a first subtree and a second subtree, wherein each remaining data point of a set of remaining data points of the respective subset is assigned to the first subtree or the second subtree based on which side of a hyperplane the remaining data point lies, the hyperplane being a hyperplane in an N-dimensional representation of the plurality of data points, each of the N dimensions corresponding to one of the plurality of variables, an N-dimensional slope vector of the hyperplane being randomly selected and each coordinate value of an N-dimensional intercept point of the hyperplane being determined by selecting a variable corresponding to a respective coordinate, determining an interval where a distance between values of the variable of consecutive data points of the remaining data points is maximal, and selecting a value for the respective coordinate on the interval; - determining (105) an average tree depth of the set of binary trees; - determining (107), for each respective data point of the plurality of data points, a path length between an external node of a binary tree of the set of binary trees and a root node of the binary tree, the external node being associated with the respective data point; - determining (109), for each respective data point, an anomaly score based on the path length determined for the respective data point and further based on the average tree depth; - detecting (111), in real-time, based on the anomaly scores, whether any of the plurality of data points corresponds to an anomaly; and - generating (113) an output signal reflecting a detected accident based on the detected anomalies.
  14. A computer program or suite of computer programs comprising at least one software code portion or a computer program product storing at least one software code portion, the software code portion, when run on a computer system, being configured for performing the method of claim 13.

Description

FIELD OF THE INVENTION The invention relates to a system for detecting accidents in motorsports. The invention further relates to a method of detecting accidents in motorsports. The invention also relates to a computer program product enabling a system to perform such a method. BACKGROUND OF THE INVENTION Motor racing is a dangerous sport. Therefore, motorsports' governing bodies strive to make racing as safe as possible. Recent innovations and new technologies make the sport safer to a great extent. However, all these safety improvements do not make the sport risk-free. It is not uncommon that a driver is involved in a hefty accident and becomes seriously injured. Racing vehicles and tracks are designed such that during a crash, drivers should be safe. A key safety moment is straight after an accident, when both the crashed drivers and the oncoming drivers are in danger. Crashed vehicles are often standing still on the racetrack. An oncoming driver could collide with such a stranded vehicle. Huge speed differences and unexpected impact angles increase the probability of serious injury. Most races are supervised by marshals. If they spot an accident, they wave with a yellow flag. This flag warns oncoming drivers that there is a dangerous situation ahead. Unfortunately, there is a delay between the accident and when the marshal begins waving. Moreover, tracks are not always equipped with a sufficient number of marshals. Anomalies are points or patterns in data that do not conform to expected behavior. Anomaly detection is a field of research in which methods to classify anomalies are analyzed and developed. Anomaly detection has particular complexities compared to other classification problems. The characteristics of an anomaly are usually unknown, such as frequency and data structure. Also, anomalies can differ significantly between each other; anomaly classes can be heterogeneous. Lastly, anomalies are rare and consequently are part of an imbalanced dataset. Anomaly detection started as a statistical significance tests to control data quality. Currently, anomaly detection has a wider range of applications, such as fraud detection in financial transactions, fault detection in manufacturing, intrusion detection in a computer network, monitoring sensor readings in an aircraft and spotting medical problems in health data. The Isolation forest algorithm is an example of an anomaly detection algorithm. The isolation forest algorithm is an adaptation of the random forest algorithm. Isolation forest uses the step-wise partition method of random forest. Random partitions are made until all data points are isolated. The main idea is that anomalies are easier to isolate and therefore require fewer partitions. The Isolation forest algorithm is described in "Isolation forest" by Liu, F. T., Ting, K. M., and Zhou, Z.-H, published in eighth IEEE international conference on data mining (2008), pages 413 - 422. The extended isolation forest algorithm is an improvement over the isolation forest algorithm. The extended isolation forest algorithm is described in "Extended isolation forest " by Hariri, S., Kind, M. C., and Brunner, R. J., published in IEEE Transactions on Knowledge and Data Engineering (2019), 33(4):1479-1489. Hariri et al found that splitting a multidimensional dataset using the isolation forest algorithm causes a bias. Isolation forest splits the dataset using only one variable. A combination of partitions might create a cluster where there are a lot of partitions, but there are no data points. Hariri et al proposed the extended isolation forest algorithm as a solution to this problem. Instead of splitting the dataset using a single variable, they split the dataset using a random hyperplane. In the standard isolation forest algorithm, the splitting process requires two pieces of information: a random variable and a random value within the range of this variable. In the extended isolation forest algorithm, the splitting process still requires two pieces of information: a random slope (vector) and a random intercept (point) for the split which is chosen from the range of available values of the training data. For every split, there is a different random hyperplane. SUMMARY OF THE INVENTION It is a first objective of the invention to provide a system, which uses anomaly detection to detect accidents in motorsports. It is a second objective of the invention to provide a method, which uses anomaly detection to detect accidents in motorsports. In a first aspect of the invention, a system for detecting accidents in motorsports comprises at least one processor configured to obtain measurement data from measurement units incorporated in or attached to a plurality of vehicles, the measurement data comprising a plurality of data points, each of the plurality of data points comprising a value for each of a plurality of variables, and to recursively partition the plurality of data points into a set of binary trees by det