Lane Change Prediction by Combining Movement and Situation based Probabilities

Wissing, Christian; Nattermann, T.; Glander, Karl-Heinz; Hass, Carsten; Bertram, Torsten

doi:10.1016/j.ifacol.2017.08.960

Cited by 30 publications

(22 citation statements)

References 10 publications

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“…An overview of motion prediction approaches is presented in [1], which distinguishes three categories: physicsbased, maneuver-based, and interaction-aware approaches. Maneuver-based approaches, which are most relevant in the context of our work, typically define three fundamental maneuver classes: lane change to the left LCL, lane change to the right LCR, and lane following F LW [2]- [4]. These maneuver classes are used to simplify modeling the entirety of highway driving and its multimodality.…”

Section: Related Workmentioning

confidence: 99%

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Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks

Wirthmüller

Klimke

Schlechtriemen

et al. 2021

IEEE Robot. Autom. Lett.

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To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this temporal information might be even more useful. This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways using long short-term memorybased recurrent neural networks. An extensive evaluation based on a large real-world data set shows that our approach is able to make reliable predictions, even in the most challenging situations, with a root mean squared error around 0.7 seconds. Already 3.5 seconds prior to lane changes the predictions become highly accurate, showing a median error of less than 0.25 seconds. In summary, this article forms a fundamental step towards downstreamed highly accurate position predictions.

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Section: Related Workmentioning

confidence: 99%

“…An approach that decomposes the lane change probability into a situation-and a movement-based component is presented in [2]. As a result, an F 1 -score better than 98 %, with the maneuvers being detected approximately 1.5 s in advance, can be obtained.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks

Wirthmüller

Klimke

Schlechtriemen

et al. 2021

IEEE Robot. Autom. Lett.

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show abstract

“…Driver intention has been widely studied in the past two decades. Previous studies related to driver lane change intention have focused on ramp [27,28], intersection [29,30], and highway [31,32] and many methodologies have been proposed. For merging different categories of behavior, a classification regression tree was created and trained [27].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

Liu

Peng

Sun

2021

Journal of Advanced Transportation

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Rapid and correct estimation of driver lane change intention plays an important role in the advanced driver assistance system (ADAS), which could make the driver improve the reliability of the ADAS system and help to decrease driver workload. In this study, a method based on the long short-term memory network (LSTM) and Dempster–Shafer evidence theory is proposed. The model consists of a preliminary decision-making label and a final decision-making label. Driver visual information, head orientation, and vehicle dynamics are collected by preliminary decision-making label. Then, LSTM is used to calculate the initial probability of the driver lane change (left, right, and lane keeping) maneuver intention. The outputs of LSTM are normalized and assigned a basic probability by the Dempster–Shafer evidence theory. The final decision-making label analyzes the information and outputs the probability of each lane change intention and the decision is to identify the driver's current intention. The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. The outcome of this work is an essential component for all levels of road vehicle automation.

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“…in (Zhang et al, 2018). In (Wissing et al, 2017), a Support Vector Machine (SVM) technique is used for the motion estimation of the surrounding vehicles.…”

Section: Introductionmentioning

confidence: 99%

Challenges and Possibilities of Overtaking Strategies for Autonomous Vehicles

Hegedüs

Németh

Gáspár

2020

Period. Polytech. Transp. Eng.

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This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The computation time of the proposed decision-making algorithms may be high, because the number of describing parameters of the traffic situations may vary in a high range. The presented clustering-based, graph-based and dynamic-based methods differ in the complexity of their computation algorithms. Since the decision-making process may require considerable online computation effort, a neural-network-based approach is presented for implementation purposes.

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Lane Change Prediction by Combining Movement and Situation based Probabilities

Cited by 30 publications

References 10 publications

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks

Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

Challenges and Possibilities of Overtaking Strategies for Autonomous Vehicles

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