Conference Paper

A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments

Inst. for Anthropomatics, Karlsruhe Inst. of Technol., Karlsruhe, Germany
DOI: 10.1109/ITSC.2010.5625262 Conference: Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
Source: IEEE Xplore


This paper presents a filter that is able to simultaneously estimate the behaviors of traffic participants and anticipate their future trajectories. This is achieved by recognizing the type of situation derived from the local situational context, which subsumes all information relevant for the drivers decision making. By explicitly taking into account the interactions between vehicles, it achieves a comprehensive situational understanding, inevitable for autonomous vehicles and driver assistance systems. This provides the necessary information for safe behavior decision making or motion planning. The filter is modeled as a Dynamic Bayesian Network. The factored state space, modeling the causal dependencies, allows to describe the models in a compact fashion and reduces the computational complexity of the inference process. The filter is evaluated in the context of a highway scenario, showing a good performance even with very noisy measurements. The presented framework is intended to be used in traffic environments but can be easily transferred to other robotic domains.

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    • "They test their approach in a simulated highway scenario. A fully probabilistic approach is presented in [4]. It uses contextual information to detect the type of situation a vehicle is in. "
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    ABSTRACT: Abstract — Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems or autonomous driving. When longer prediction hori- zons are needed, e.g., in decision making or motion planning, the uncertainty induced by incomplete environment perception and stochastic situation development over time cannot be neglected without sacrificing robustness and safety. Especially describing the unknown behavior of other traffic participants poses a complex problem. Building consistent probabilistic models of their manifold and changing interactions with the environment, the road network and other traffic participants by hand is error- prone. Further, the results could hardly cover the complete diversity of human behaviors. This paper presents an approach for learning continuous, non-linear, context dependent process models for the behavior of traffic participants from unlabeled observations. The resulting models are naturally embedded into a Dynamic Bayesian Network (DBN) that enables the prediction and estimation of traffic situations based on noisy and incomplete measurements. Using a hybrid state repre- sentation it combines discrete and continuous quantities in a mathematically sound way. Experiments show a significant improvement in estimation and prediction accuracy by the learned context dependent models over standard models, which only consider vehicle dynamics.
    International IEEE Conference on Intelligent Transportation Systems (ITSC); 10/2013
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    • "Another solution is to use Monte-Carlo simulation to explore the dierent possible realizations of a maneuver by sampling on the input variables [16]. Other works propose integrated frameworks based on Markov State Space Models (MSSM) which explicitly represent the maneuver intention of drivers and allow the prediction of future trajectories without the need of a separate maneuver intention estimation step [17] [18] [19]. "
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    ABSTRACT: This work tackles the risk estimation problem from a new perspective: a framework is proposed for reasoning about traffic situations and collision risk at a semantic level, while classic approaches typically reason at a trajectory level. Risk is assessed by estimating the intentions of drivers and detecting conflicts between them, rather than by predicting the future trajectories of the vehicles and detecting collisions between them. More specifically, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do according to the traffic rules. The reasoning about intentions and expectations is performed in a probabilistic manner, in order to take into account sensor uncertainties and interpretation ambiguities. This framework can in theory be applied to any type of traffic situation; here we present its application to the specific case of road intersections. The proposed motion model takes into account the mutual influences between the maneuvers performed by vehicles at an intersection. It also incorporates information about the influence of the geometry and topology of the intersection on the behavior of a vehicle, and therefore can be applied to arbitrary intersection layouts. The approach was validated with field trials using passenger vehicles equipped with Vehicle-to-Vehicle wireless communication modems, and in simulation. The results demonstrate that the algorithm is able to detect dangerous situations early and complies with real-time constraints.
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    • "Motivated by this prospect, driver intent inference has been an important research issue for more than a decade by now. Common approaches include discriminative methods such as support vector machines [3], relevance vector machines [4], conditional random fields [5] and neural networks [6] as well as generative approaches such as hidden Markov models [7] and dynamic Bayesian networks [8], [9]. Discriminative approaches usually aim to predict a single type of maneuver while generative models are more often applied if the probability distribution over a set of available maneuvers is to be inferred [10]. "
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    ABSTRACT: Reasoning about the driver intent is fundamental both to advanced driver assistance systems as well as to highly automated driving. In contrast to the vast majority of preceding work, we investigate an architecture that can deal with arbitrary combinations of subsequent maneuvers as well as a varying set of available features. Detailed parametric models are given for the indicator, velocity and gaze direction features, all of which are parametrized from the results of extensive user studies. Evaluation is carried out for continuous right-turn prediction on a separate data set. Assuming conditional independence between the individual feature likelihoods, we investigate the contribution of each feature to the overall classification result separately. In particular, the approach is shown to work well even when faced with implausible observations of the indicator feature.
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