Probabilistic situation assessment framework for multiple, interacting traffic participants in generic traffic scenes

Klingelschmitt, Stefan; Damerow, Florian; Willert, Volker; Eggert, Julian

doi:10.1109/ivs.2016.7535533

Cited by 23 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work in [23] presents experience-based data on the interaction between the ego and surrounding vehicles during lane changes. In order to consider interaction in situation assessment, one can compute an interaction-aware joint probability distribution [24] or detect conflicting intentions at intersections by comparing what vehicles intend to do with what they are expected to do [25].…”

Section: Introductionmentioning

confidence: 99%

Interaction-aware occupancy prediction of road vehicles

Koschi

Althoff

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

A crucial capability of autonomous road vehicles is the ability to cope with the unknown future behavior of surrounding traffic participants. This requires using nondeterministic models for prediction. While stochastic models are useful for long-term planning, we use set-valued nondeterminism capturing all possible behaviors in order to verify the safety of planned maneuvers. To reduce the set of solutions, our earlier work considers traffic rules; however, it neglects mutual influences between traffic participants. This work presents the first solution for establishing interaction within set-based prediction of traffic participants. Instead of explicitly modeling dependencies between vehicles, we trim reachable occupancy regions to consider interaction, which is computationally much more efficient. The usefulness of our approach is demonstrated by experiments from the CommonRoad benchmark repository.

show abstract

Section: Introductionmentioning

confidence: 99%

Interaction-aware occupancy prediction of road vehicles

Koschi

Althoff

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…However, it is hard to directly combine the predictions of single entities since the future motions may be exclusive. Instead, situation prediction [25] [26] can provide the joint distribution of the motions of surroundings.…”

Section: Output Variation 1: Number Of Predicted Entitiesmentioning

confidence: 99%

“…For example, there can be several motion patterns for vehicles near stop signs such as conservative/normal stop and rolling/moderate/severe violation [38], which can be clustered via unsupervised learning. The longitudinal motions of vehicles can be simplified as acceleration, deceleration and expected behavior pattern [25]. In [10], the motion of the predicted vehicle impacted by the proceeding one was simplified as free drive and influenced, and the left-turn motion yielding the oncoming vehicle was simplified as full stop and slow down.…”

Section: B Motion Patterns With Hierarchical Categorizationmentioning

confidence: 99%

Probabilistic Prediction from Planning Perspective: Problem Formulation, Representation Simplification and Evaluation Metric

Zhan

Fortelle

Chen

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Accurate probabilistic prediction for intention and motion of road users is a key prerequisite to achieve safe and high-quality decision-making and motion planning for autonomous driving. Typically, the performance of probabilistic predictions was only evaluated by learning metrics for approximation to the motion distribution in the dataset. However, as a module supporting decision and planning, probabilistic prediction should also be evaluated from decision and planning perspective. Moreover, the evaluation of probabilistic prediction highly relies on the problem formulation variation and motion representation simplification, which lacks a summary in a comprehensive framework. To address such concerns, we provide a systematic and unified framework for the analysis of three under-explored aspects of probabilistic prediction: problem formulation, representation simplification and evaluation metric. More importantly, we address the omitted but crucial problems in the three aspects from decision and planning perspective. In addition to a review of learning metrics, metrics to be considered from planning perspective are highlighted, such as planning consequence of inaccurate and erroneous prediction, as well as violations of predicted motions to planning constraints. We address practical formulation variations of prediction problems, such as decision-maker view and blind view for viewpoint, as well as reactive prediction for interaction, so that decision and planning can be facilitated.

show abstract

“…Many approaches have been used to model interactions within multiple traffic participants. The model-based methods provide a straightforward understanding but maybe only suitable for limited scenarios [9]. The dynamic Bayesian networks [2] and deep neural networks [4] are powerful in inferring hidden states of traffic behavior, but the states should be in a fixed space.…”

Section: Introductionmentioning

confidence: 99%

A General Framework of Learning Multi-Vehicle Interaction Patterns from Video

Zhang

Zhu

Wang

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

Semantic learning and understanding of multivehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.

show abstract

Probabilistic situation assessment framework for multiple, interacting traffic participants in generic traffic scenes

Cited by 23 publications

References 19 publications

Interaction-aware occupancy prediction of road vehicles

Interaction-aware occupancy prediction of road vehicles

Probabilistic Prediction from Planning Perspective: Problem Formulation, Representation Simplification and Evaluation Metric

A General Framework of Learning Multi-Vehicle Interaction Patterns from Video

Contact Info

Product

Resources

About