Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection

Girma, Abenezer; Amsalu, Seifemichael B.; Workineh, Abrham; Khan, Mubbashar Altaf; Homaifar, Abdollah

doi:10.1109/iv47402.2020.9304785

Cited by 15 publications

(6 citation statements)

References 19 publications

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“…The precision for the left and right turn intentions were respectively 77.0% and 76.6%, while the lane change precision was 45.9% for the left lane change and 43.6% for the right lane change maneuver. Girma et al (2020) [80] used 2970 data points of which 990 were labeled as driving straight, 770 as stopping, 660 turning right, and 550 turning left at an intersection. Only the velocity and yaw rate were used as categorized sequence features as input to a bidirectional LSTM with an attention mechanism and yielded an accuracy of 99.65%.…”

Section: 33%mentioning

confidence: 99%

Driver Intention Recognition: State-of-the-Art Review

Vellenga

Steinhauer

Karlsson

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Every year worldwide more than one million people die and a further 50 million people are injured in traffic accidents. Therefore, the development of car safety features that actively support the driver in preventing accidents, is of utmost importance to reduce the number of injuries and fatalities. However, to establish this support it is necessary that the advanced driver assistance system (ADAS) understands the driver's intended behavior in advance. The growing variety of sensors available for vehicles together with improved computer vision techniques, hence led to increased research directed towards inferring the driver's intentions. This article reviews 64 driver intention recognition studies with regard to the maneuvers considered, the driving features included, the AI methods utilized, the achieved performance within the presented experiments, and the open challenges identified by the respected researchers. The article provides a high level analysis of the current technology readiness level of driver intention recognition technology to address the challenges to enable reliable driver intention recognition, such as the system architecture, implementation, and the purpose of the technology.

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Section: 33%mentioning

confidence: 99%

Driver Intention Recognition: State-of-the-Art Review

Vellenga

Steinhauer

Karlsson

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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“…Abenezer Girma et al [18] used deep bidirectional long and short-term memory (LSTM) and an attention mechanism model based on the hybrid state system (HSS) framework in their study to solve the problem of estimating driver behavior near road intersections. However, they only worked on image datasets.…”

Section: Testmentioning

confidence: 99%

A Study on Safety Driving of Intelligent Vehicles Based on Attention Mechanisms

Ding¹,

Jian²

2022

ECTI-CIT Transactions

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Intelligent vehicles attempt to improve the daily transportation of people, but their safety has been questioned after numerous trafic accidents. This paper proposes a Safety Driving Framework (SDF) to improve the ability of intelligent vehicles to avoid risks during emergencies. When performing autonomous driving tasks, the SDF interacts with the environment using a camera, makes decisions using convolutional neural networks, and can perform an emergency stop if it encounters an obstacle. In this paper, we examine the potential of attention mechanisms to enhance the performance of convolutional neural networks and construct four convolutional neural networks with attention mechanisms to use in experiments. Additionally, we extend a dataset and enhance the robustness of the model by implementing data augmentation (DA) techniques. We train the model using 10-fold cross-validation. In this article, we build an intelligent driving platform and a simulation track for simulation testing. The experimental results show that CNNs using data reinforcement and with an attention mechanism perform better than existing models. In particular, ENetb0-SE has an average recognition rate of 95.6% for obstacles and an accident rate of 2%, which is much better than existing models.

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“…An average accuracy of 85% was achieved. In another work, Girma et al [23] proposed an intention prediction approach using LSTM architecture with Attention module. The authors analyzed the velocity and the yaw rate of the vehicle before the intersection to classify the maneuvers into four categories, including right/left turning, keeping the path, and stopping at the traffic light.…”

Section: Related Workmentioning

confidence: 99%

Toward Driver Intention Prediction for Intelligent Vehicles: A Deep Learning Approach

Azadani

Boukerche

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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High-level scene understanding and situational awareness are fundamental for autonomous vehicles before being widely used on public roads in a thoroughly efficient and safe manner. These tasks involve not only perceiving the current surrounding states but also predicting the future behavior of nearby human-driven vehicles. A large amount of vehicular sensing data can be collected using sensors and networking systems in vehicles. Moreover, with the advent of Dedicated Short Range Communication, vehicles can further transfer intention information to surrounding vehicles. However, real-time intention inference of nearby drivers is still challenging. Our primary focus is to present a novel deep learning-based approach to predict drivers driving behavior at unsignalized T-junctions. We use temporal convolutional networks to analyze sequences of trajectory data for a vehicle approaching the T-junction and classify the maneuver seconds before actual maneuver occurrence. The cross-validation results demonstrate the efficiency of the proposed methodology.

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Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection

Cited by 15 publications

References 19 publications

Driver Intention Recognition: State-of-the-Art Review

Driver Intention Recognition: State-of-the-Art Review

A Study on Safety Driving of Intelligent Vehicles Based on Attention Mechanisms

Toward Driver Intention Prediction for Intelligent Vehicles: A Deep Learning Approach

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