Neural network for lane change prediction assessing driving situation, driver behavior and vehicle movement

Leonhardt, Veit; Wanielik, Gerd

doi:10.1109/itsc.2017.8317832

Cited by 24 publications

(22 citation statements)

References 10 publications

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“…The multilayer feed forward network (MLF) is the most commonly used network. The ANN approach performs well in many pattern-classification applications [34], [35]. The number of processing elements in the input layer corresponds to the number of features obtained in the maneuver dataset.…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction

et al. 2020

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Innovative technologies and naturalistic driving data sources provide a great potential to develop reliable autonomous driving systems. Understanding the behaviors of surrounding vehicles is essential for improving safety and mobility of autonomous vehicles. Onboard sensors like Radar, Lidar and Camera are able to track surrounding vehicles motion and to get different features like position, velocity and yaw. This paper proposes a hybrid approach to integrate maneuver classification using neural networks and trajectory prediction using Long Short-term Memory (LSTM) networks to get the future positions of adjacent vehicles. In this study we use the Next Generation Simulation (NGSIM) public dataset that provides a real driving data. The proposed approach is validated experimentally using VEDECOM demonstrator data. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 2.2 seconds in advance. The Root Mean Square (RMS) errors of lateral and longitudinal positions are 0.30 m and 3.1 m respectively. The results demonstrate a high performance compared to various existing methods.

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Section: A Artificial Neural Networkmentioning

confidence: 99%

Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction

et al. 2020

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“…e purpose of ADA is to improve the passengers' safety as well as to reduce drivers' interaction in dynamic environments often caused by imprecise decisionmaking or errors associated with the human nature. Most of the works focus on localization and tracking of driving behavior to maintain the driver guidance throughout routing [21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Benalla

Achchab

Hrimech

2020

Journal of Advanced Transportation

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Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver assistance systems (ADAS), ITS are strongly concerned with data fusion techniques of all kinds of sensors deployed over the traffic network. Driver assistance is devoid of a comprehensive evidential reasoning system on contextual information, more specifically when a combination involves inside and outside sensory information of the driving environment. In this paper, we propose a novel agent-based evidential reasoning system using contextual information. Based on a series of information handling techniques, specifically, the belief functions theory and heuristic inference operations to achieve a consensus about daily driving activity in automatically inferring. That is quite different from other existing proposals, as it deals jointly with the driving behavior and the driving environment conditions. A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research.

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“…Other researchers have employed this technique to learn overtaking and lane-changing behaviour, predicting lateral and longitudinal vehicle movement, etc. [16][17][18]. Ou and Karray [19] use a deeplearning approach to predict lane change and turns prior to a green light.…”

Section: Related Work On Modelling Driver Behaviourmentioning

confidence: 99%

“…We also look at correlating the cephalo‐ocular behaviour with the driving tasks. While gaze and head position have been recognised as important in driving, it is only recently that this kind of data has begun to be considered in conjunction with other driver data [12, 13, 18]. Cephalo‐ocular behaviour is very prominent in the driving context, but it is significantly different for each manoeuvre any driver performs and there are different cephalo‐ocular responses for the same manoeuvre among drivers.…”

Section: Framework For Developing Predictive Models Of Driving Behamentioning

confidence: 99%

Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

Rahman

Beauchemin

Bauer

2020

IET intell. transp. syst

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This work introduces and evaluates a model for predicting driver behaviour, namely turns or proceeding straight, at traffic light intersections from driver three-dimensional gaze data and traffic light recognition. Based on vehicular data, this work relates the traffic light position, the driver's gaze, head movement, and distance from the centre of the traffic light to build a model of driver behaviour. The model can be used to predict the expected driver manoeuvre 3 to 4 s prior to arrival at the intersection. As part of this study, a framework for driving scene understanding based on driver gaze is presented. The outcomes of this study indicate that this deep learning framework for measuring, accumulating and validating different driving actions may be useful in developing models for predicting driver intent before intersections and perhaps in other key-driving situations. Such models are an essential part of advanced driving assistance systems that help drivers in the execution of manoeuvres.

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Neural network for lane change prediction assessing driving situation, driver behavior and vehicle movement

Cited by 24 publications

References 10 publications

Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction

Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

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