A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

Bahram, Mohammad; Hubmann, Constantin; Lawitzky, Andreas; Aeberhard, Michael; Wollherr, Dirk

doi:10.1109/tits.2015.2506642

Cited by 155 publications

(87 citation statements)

References 32 publications

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“…It is even harder for a human driver to predict surrounding vehicles' behaviors over such a long term. Most previous studies just try to improve the prediction performance of the lane-changing behavior (LCL, LCR), but the precision of LK was neglected [14,16]. In this study, we found that LK is pretty hard to predict for the urban roadways (the lowest precision in Figure 11).…”

Section: Quantitative Evaluationmentioning

confidence: 70%

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A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Geng

Liang

et al. 2017

Applied Sciences

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Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles' driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario's characteristics. Finally, the target vehicle's future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.

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Section: Quantitative Evaluationmentioning

confidence: 70%

“…[14]. For the junction-related maneuvers (TL, TR, GS, SS), the Time to Intersection TTI, and longitudinal velocity v lon , v lat of the target vehicle were chosen [12].…”

Section: Problem Formulationmentioning

confidence: 99%

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Geng

Liang

et al. 2017

Applied Sciences

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“…Due to the highly unequal class incidences, balanced measures [6] must be applied for classification perfor-…”

Section: Evaluation Of Maneuver Detectionmentioning

confidence: 99%

Prediction of highway lane changes based on prototype trajectories

Augustin¹,

Hofmann²,

Konigorski

2019

Forsch Ingenieurwes

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The vision of automated driving is to increase both road safety and efficiency, while offering passengers a convenient travel experience. This requires that autonomous systems correctly estimate the current traffic scene and its likely evolution. In highway scenarios early recognition of cut-in maneuvers is essential for riskaware maneuver planning. In this paper, a statistical approach is proposed, which advantageously utilizes a set of prototypical lane change trajectories to realize both early maneuver detection and uncertaintyaware trajectory prediction for traffic participants. Generation of prototype trajectories from real traffic data is accomplished by Agglomerative Hierarchical Clustering. During clustering, the alignment of the cluster prototypes to each other is optimized and the cohesion of the resulting prototype is limited when two clusters merge. In the prediction stage, the similarity of observed vehicle motion and typical lane change patterns in the data base is evaluated to construct a set of significant features for maneuver classification via Boosted Decision Trees. The future trajectory is predicted combining typical lane change realizations in a mixture model. B-splines based trajectory adaptations guarantee continuity during transition from actually observed to predicted vehicle states. Quantitative evaluation results demonstrate the proposed concept's improved performance for both maneuver and trajectory prediction compared to a previously implemented reference approach.

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“…For this purpose, the set of possible intentions is typically predefined offline and the models are learned for these fixed number of classes. For highways, this set usually consists of lane change left, lane change right, and keep lane (e.g., [7], [8]). For intersections, the desired route is mostly represented by the turning directions left, right, and straight (e.g., [5], [6]).…”

Section: A Intention Estimationmentioning

confidence: 99%

“…As these profiles only depend on features of the current situation (e.g., states of preceding vehicles), but do not incorporate the prediction of the surrounding vehicles, future interdependencies are ignored. Another combined approach can be found in [8], where highway maneuvers are first estimated based on multi-agent simulations and then used as input for a continuous trajectory prediction. Thus, both works solve the two problems separately, but improve their trajectory prediction by their maneuver and route estimates.…”

Section: Estimation and Predictionmentioning

confidence: 99%

Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

Schulz

Hubmann

Lochner

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between their trajectories. We present a probabilistic prediction framework based on a dynamic Bayesian network, which represents the state of the complete scene including all agents and respects the aforementioned dependencies. We propose Markovian, contextdependent motion models to define the interaction-aware behavior of drivers. At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference. Secondly, we perform a probabilistic forward simulation of the network's estimated belief state to generate the different combinatorial scene developments. This provides the corresponding trajectories for the set of possible, future scenes. Our framework can handle various road layouts and number of traffic participants. We evaluate the approach in online simulations and real-world scenarios. It is shown that our interaction-aware prediction outperforms interaction-unaware physics-and map-based approaches.

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A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

Cited by 155 publications

References 32 publications

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Prediction of highway lane changes based on prototype trajectories

Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

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