Driving Intention Recognition and Lane Change Prediction on the Highway

Han, Teawon; Jing, Junbo; Özgüner, Ü.

doi:10.1109/ivs.2019.8813987

Cited by 35 publications

(19 citation statements)

References 26 publications

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“…Notably, for cut-in and cut-out, two behavior that have a significant impact on the driving status of ego-vehicle, our model achieves the best performance compared to Social-LSTM and has increased by 6.2% and 6.9% respectively, and the overall prediction accuracy has improved significantly. In the multi-parameter evaluation system, it is compared with dynamic Bayesian networks [35], HSS based LSTM [36], attention-based LSTM [37], and Bayesian networks [38], as shown in Table 3. As shown in Table 3, compared with the dynamic Bayesian network, the F1 score, precision, and accuracy are increased by 16.2%, 31.2%, and 36.9%, respectively, and the recall is slightly reduced by 8.8%.…”

Section: Results Analysismentioning

confidence: 99%

Vehicle Interaction Behavior Prediction with Self-Attention

Xin

Lian

et al. 2022

Sensors

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The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

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Section: Results Analysismentioning

confidence: 99%

Vehicle Interaction Behavior Prediction with Self-Attention

Xin

Lian

et al. 2022

Sensors

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“…1) Probabilistic graphical models: Predominantly three types of probabilistic graphical models (PGMs) are used in DIR studies: Bayesian networks (BN) [24], dynamic Bayesian networks (DBN) [25,26], and hidden Markov models (HMM) [27].…”

Section: B Driver Intention Recognition Methodsmentioning

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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“…In recent years, deep neural network (DNN) has received great attention in sequence prediction [15][16][17] . Recurrent [18][19][20] . Various network models based on different branches of ordinary RNN networks are applied to research topics such as behaviour classification, trajectory prediction.…”

Section: Methods Of Lane Change Predictionmentioning

confidence: 99%

“…Rule based methods usually group the autonomous vehicles behaviours according to driving rules or driving knowledge. Finite state machine is the typical method, which has the advantages of clear logical relationship, strong practicability [19] . The finite state machine method has strong applicability and stable structure.…”

Section: Rule Based Autonomous Decision-makingmentioning

confidence: 99%

Research prospect of autonomous driving decision technology under complex traffic scenarios

2022

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Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

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Driving Intention Recognition and Lane Change Prediction on the Highway

Cited by 35 publications

References 26 publications

Vehicle Interaction Behavior Prediction with Self-Attention

Vehicle Interaction Behavior Prediction with Self-Attention

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

Research prospect of autonomous driving decision technology under complex traffic scenarios

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