Dual approach for maneuver classification in vehicle environment data

Sonka, Adrian; Krauns, Florian; Henze, Roman; Küçükay, Ferit; Katz, Roman; Lages, Ulrich

doi:10.1109/ivs.2017.7995704

Cited by 11 publications

(2 citation statements)

References 5 publications

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“…Following data post-processing, to generate clear lane-related object tracks, cut-in scenarios from the right were detected, following the definitions above and rule-based detection algorithms, extended by a dual approach for lane change detection, as elaborated in detail in [39]. For the current study, the beginning of a cut-in scenario was set when the challenging vehicle's lateral distance to its left lane became lower than 1.5 m and had a positive lateral velocity.…”

Section: Cut-in Scenario Definition Detection and Groupingmentioning

confidence: 99%

Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

et al. 2021

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Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios.

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Section: Cut-in Scenario Definition Detection and Groupingmentioning

confidence: 99%

Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

et al. 2021

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“…Researchers have proposed many feature-based classifiers for vehicle behavior classification, including SVMs [5], [6], HMMs [7]- [10], Naïve Bayes Classification [12], Logistic Regression [12] and LSTM [13]. These methods use motionbased features such as speed, acceleration, yaw rate and other context information such as lane position, turn signals, distance from the leading vehicle to implement the classifier.…”

Section: Related Workmentioning

confidence: 99%

A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN

Zhang

Nan

Yang

et al. 2021

Preprint

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In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision. In this paper, we propose a neural network model based on trajectories information for driving behavior recognition. Unlike existing trajectory-based methods that recognize the driving behavior using the handcrafted features or directly encoding the trajectory, our model involves a Multi-Scale Convolutional Neural Network (MSCNN) module to automatically extract the high-level features which are supposed to encode the rich spatial and temporal information. Given a trajectory sequence of an agent as the input, firstly, the Bi-directional Long Short Term Memory (Bi-LSTM) module and the MSCNN module respectively process the input, generating two features, and then the two features are fused to classify the behavior of the agent. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.

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