Driver monitoring algorithm for advanced driver assistance systems

Simić, Aleksandra; Kocic, Ognjen; Bjelica, Milan Z.; Milosevic, Milena

doi:10.1109/telfor.2016.7818908

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…A personalized LCPM based on deep learning method, was constructed to predict the lane change intention of the driver through analyzing the ego vehicle dynamics data and driver physiological data [18]. A novel driver monitoring algorithm was proposed to monitor the driver's facial expression and then decided which operation would be performed, which could improve the performance of ADAS [19]. However, in order to predict the lane change intention of other vehicles, the driver physiological data is not available.…”

Section: Introductionmentioning

confidence: 99%

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

et al. 2020

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Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine).INDEX TERMS Intelligent vehicle, lane change, driving intention prediction, advanced assisted driving systems, multi-LSTM.

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Section: Introductionmentioning

confidence: 99%

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

et al. 2020

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“…Sensors such as cameras, LiDAR, ultrasonic sensors, and RADAR can be used to perceive the environment around the ego-vehicle under different circumstances. An efficient technology, which involves fusing information from a point cloud (generated by LiDAR) and an image (generated by camera), is discussed by Kocic et al [ 18 ]. Accordingly, we shall use the LiDAR–camera combination of sensors in our work.…”

Section: Background and Previous Workmentioning

confidence: 99%

“…Emergency brake assist (EBA) is an ADAS system that assists the driver in avoiding a collision or decreasing the impact of collision with other vehicles or vulnerable road users when the collision is unavoidable [ 16 , 17 ]. Research shows that in many critical situations, human drivers tend to react either too late or in a wrong way [ 18 ]. In such scenarios, the best alternative is to apply the vehicle brakes with the safe maximum force to minimise the consequences of the unavoidable impact [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

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“…It also means that the driver is trusting in the DAS. This paper does not propose a specific mathematical model to estimate the driver's trust in the DAS, but a machine learning-based monitoring system [36] is highly expected.…”

Section: Over-trust Inference Modelmentioning

confidence: 99%

Driving behavior model considering driver's over-trust in driving automation system

Liu

Hiraoka

2019

Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct

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As various driving automation system (DAS) are commonly used in the vehicle, the over-trust in the DAS may put the driver in the risk. In order to prevent the over-trust while driving, the trust state of the driver should be recognized. However, description variables of the trust state are not distinct. This paper assumed that the outward expressions of a driver can represent the trust state of him/her-self. The explicit behaviors when driving with DAS is seen as those outward expressions. In the experiment, a driving simulator with a driver monitoring system was used for simulating a vehicle with the adaptive cruise control (ACC) and observing the motion information of the driver. Results show that if the driver completely trusted in the ACC, then 1) the participants were likely to put their feet far away from the pedals; 2) the operational intervention of the driver will delay in dangerous situations. In the future, a machine learning model will be tried to predict the trust state by using the motion information of the driver.

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Driver monitoring algorithm for advanced driver assistance systems

Cited by 15 publications

References 6 publications

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Driving behavior model considering driver's over-trust in driving automation system

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