Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning

Méneroux, Yann; Guilcher, Arnaud Le; Pierre, Guillaume; Hamed, Maher M.; Mustière, Sébastien; Orfila, Olivier

doi:10.1007/s41060-019-00197-x

Cited by 16 publications

(11 citation statements)

References 41 publications

(27 reference statements)

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“…Using a mixture of physical 1 and statistical 2 features, both supervised (Random Forests) and unsupervised methods (Spectral clustering) are tested for a 3-class classification problem (stop-signs, traffic lights, uncontrolled intersections), achieving an over 90% classification accuracy. Moreover, Meneroux et al (2020) detect traffic lights using speed-profiles. By testing three different ways of deriving features (speed-profiles), namely 1. functional analysis of speed logs, 2. raw speed measurements and, 3. image recognition technique, they demonstrate that the functional description of speed profiles with wavelet transforms, outperforms the other approaches.…”

Section: Related Workmentioning

confidence: 99%

Trajectory analysis at intersections for traffic rule identification

Wang

Zourlidou

Golze

et al. 2020

Geo-spatial Information Science

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In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed-and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.).

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Section: Related Workmentioning

confidence: 99%

Trajectory analysis at intersections for traffic rule identification

Wang

Zourlidou

Golze

et al. 2020

Geo-spatial Information Science

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“…train in city X and test in city Y, to show the transferability of the learned models). Meneroux et al (2019) addressed the problem of detecting traffic lights by suggesting a speed-profile-based method under a classification perspective. By testing three different ways of deriving features, they demonstrate that a functional description of speed profiles with wavelet transformation outperforms the other approaches (raw speed measurements and image recognition technique).…”

Section: Related Workmentioning

confidence: 99%

Traffic Regulator Detection Using GPS Trajectories

Golze

Zourlidou

Sester

2020

KN J. Cartogr. Geogr. Inf.

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This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research.

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“…In this paper, we propose a sequence-to-sequence deep learning model using GPS tracks as a time sequence for realizing the goal mentioned above. The majority of previous works suggests using the statistical features extracted from GPS tracks, such as stop times and stop duration [5,25,26], slowdown and standstill events [27][28][29] or speed-profiles [30][31][32]. These types of features summarize the dynamics of vehicles' motion in the relevant junctions over time.…”

Section: From Gps-tracks To Traffic-regulator Detectionmentioning

confidence: 99%

“…It reports high recall but low precision and F-measure for predicting traffic-light regulator. Similarly, Méneroux et al [32] detect traffic-signals by using speed-profiles. They test three different ways of deriving features: functional analysis of speed logs, raw speed measurements and image recognition technique.…”

Section: Related Workmentioning

confidence: 99%

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

Cheng¹,

Zourlidou²,

Sester³

2020

IJGI

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Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.

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Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning

Cited by 16 publications

References 41 publications

Trajectory analysis at intersections for traffic rule identification

Trajectory analysis at intersections for traffic rule identification

Traffic Regulator Detection Using GPS Trajectories

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

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