Attention-based Lane Change Prediction

Scheel, Oliver; Nagaraja, Naveen Shankar; Schwarz, Loren; Navab, Nassir; Tombari, Federico

doi:10.1109/icra.2019.8793648

Cited by 40 publications

(30 citation statements)

References 17 publications

(29 reference statements)

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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%

“…This paper proposes a target vehicle road-right change intention prediction method based on the interactive information of a five-vehicle In the interweaving area, which is not considered in SA-LATM [33] and SVM + ANN [34], this paper realizes that it does not depend on the external environment information to effectively predict the interaction intentions of the surrounding vehicles to the ego-vehicle. The A-LSTM [37] uses the distance of the on-/off-ramp to solve the prediction in the interweaving area. However, there are various forms of access ramps in the expressway scene.…”

Section: Discussionmentioning

confidence: 99%

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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%

Section: Discussionmentioning

confidence: 99%

Vehicle Interaction Behavior Prediction with Self-Attention

Xin

Lian

et al. 2022

Sensors

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show abstract

“…In [27], a new spatial–temporal attention model validated on NGSIM data sets [28] was proposed to alleviate the problem that past attention mechanisms in the trajectory task lacked explainability. The authors in [29, 30] are also excellent works that used attention‐based RNN models to do the trajectory prediction task.…”

Section: Related Workmentioning

confidence: 99%

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

Yan

Zifeng

Yin

et al. 2020

IET intell. transp. syst

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It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence‐to‐sequence prediction problem. They tackle this problem with an LSTM encoder–decoder framework. Moreover, they propose two spatial‐attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial‐attention mechanisms adopt the selective‐attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors’ methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state‐of‐the‐art methods. Code is available at https://github.com/momo1986/Spatial-attention.

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“…It mainly consists of mandatory lane change (to make drivers keep following their route) and discretionary lane change (to pursue a better driving condition) [13]. Some studies indicated that a lane change is one of the major sources of traffic oscillations and collision risks [14]. In previous studies, traditional lane change models only consider unidirectional effects of surrounding vehicles on the subject vehicle, such as Gipps Model and Minimising Overall Braking Induced by Lane Changes (MOBIL) [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, this assumption is over‐simple due to the effects of interactions between the subject vehicle and its surrounding vehicles on lane change decisions actually exist in the lane change process. To better understand the decision‐making of a lane change, a game‐theoretical approach was used to quantitatively characterise the interactions between human drivers [14]. This approach has been widely applied in recent studies [15–18].…”

Section: Introductionmentioning

confidence: 99%

Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory‐based lane change strategy

Zheng

Ding²,

2020

IET intell. transp. syst

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Discretionary lane change is an essential part of connected and automated vehicles (CAVs) on freeway segments. Most existing studies were conducted to optimise the individual decision of discretionary lane change of CAVs. However, the effects of motion states and discretionary lane change decisions from the surrounding vehicles via vehicle to vehicle communication were ignored. To address such a problem, a game theory‐based lane change strategy is proposed to collaborate and optimise decisions of discretionary lane change between the CAVs. The payoff functions are formulated for three types of decision games and the payoff of each decision is quantitatively calculated considering the state information of surrounding vehicles. The Nash equilibrium is applied to find the optimal decision set for players. A simulation platform of a CAV environment built is used to conduct the simulation experiments. Various metrics are employed to evaluate the proposed strategy, such as total travel delay, surrogate safety measurement and wave number. The results show that the proposed lane change strategy using a game‐theoretical approach can effectively improve traffic operation, safety and oscillations compared to the baseline strategy. The proposed lane change strategy can further benefit the implementations of the CAVs.

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Attention-based Lane Change Prediction

Cited by 40 publications

References 17 publications

Vehicle Interaction Behavior Prediction with Self-Attention

Vehicle Interaction Behavior Prediction with Self-Attention

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory‐based lane change strategy

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