Tobias Gindele

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

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.

Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

Building a model of the environment is essential for mobile robotics. It allows the robot to reason about its sourroundings and plan actions according to its intentions. To enable safe motion planning it is vital to anticipate object movements. This paper presents an improved formulation for occupancy filtering. Our approach is closely related to the Bayesian Occupancy Filter (BOF) presented in [4]. The basic idea of occupancy filters is to represent the environment as a 2-dimensional grid of cells holding information about their state of occupancy and velocity. To improve the accuracy of predictions, prior knowledge about the motion preferences is used, derived from map data that can be obtained from navigation systems. In combination with a physically accurate transition model, it is possible to estimate the environment dynamics. Experiments show that this yields reliable estimates even for occluded regions.

Probabilistic Decision-Making under Uncertainty for Autonomous Driving Using Continuous POMDPs

This paper presents a generic approach for tactical decision-making under uncertainty in the context of driving. The complexity of this task mainly stems from the fact that rational decision-making in this context must consider several sources of uncertainty: The temporal evolution of situations cannot be predicted without uncertainty because other road users behave stochastically and their goals and plans cannot be measured. Even more important, road users are only able to perceive a tiny part of the current situation with their sensors because measurements are noisy and most of the environment is occluded. In order to anticipate the consequences of decisions a probabilistic approach, considering both forms of uncertainty, is necessary. We address this by formulating the task of driving as a continuous Partially Observable Markov Decision Process (POMDP) that can be automatically optimized for different scenarios. As driving is a continuous-space problem, the belief space is infinite-dimensional. We do not use a symbolic representation or discretize the state space a priori because there is no representation of the state space that is optimal for every situation. Instead, we employ a continuous POMDP solver that learns a good representation of the specific situation.

Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning

Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems and autonomous driving. When longer prediction horizons 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. Building consistent probabilistic models of drivers interactions with the environment, the road network and other traffic participants poses a complex problem. In this paper, we model the decision making process of drivers by building a hierarchical Dynamic Bayesian Model that describes physical relationships as well as the drivers behaviors and plans. This way, the uncertainties in the process on all abstraction levels can be handled in a mathematically consistent way. As drivers behaviors are difficult to model, we present an approach for learning continuous, non-linear, context-dependent models for the behavior of traffic participants. We propose an Expectation Maximization (EM) approach for learning the models integrated in the DBN from unlabeled observations. Experiments show a significant improvement in estimation and prediction accuracy over standard models which only consider vehicle dynamics. Finally, a novel approach to tactical decision making for autonomous driving is outlined. It is based on a continuous Partially Observable Markov Decision Process (POMDP) that uses the presented model for prediction.

Learning context sensitive behavior models from observations for predicting traffic situations

Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems or autonomous driving. When longer prediction horizons 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 representation 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.

Situation classification for cognitive automobiles using case-based reasoning

Driving a car in urban areas autonomously requires the ability of an in-depth analysis of the current situation. For understanding the current situation and deducing consequences for the execution of behaviors (maneuvers), higher-level reasoning about the situation has to take place. In this paper, an approach for situation interpretation for cognitive automobiles is presented. The approach relies on case-based reasoning to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

Probabilistic MDP-behavior planning for cars

This paper presents a method for high-level decision making in traffic environments. In contrast to the usual approach of modeling decision policies by hand, a Markov Decision Process (MDP) is employed to plan the optimal policy by assessing the outcomes of actions. Using probability theory, decisions are deduced automatically from the knowledge about how road users behave over time. This approach does neither depend on an explicit situation recognition nor is it limited to only a variety of situations or types of descriptions. Hence it is versatile and powerful. The contribution of this paper is a mathematical framework to derive abstract symbolic states from complex continuous temporal models encoded as Dynamic Bayesian Networks (DBN). For this purpose discrete MDP states are interpreted by random variables. To make computation feasible this space grows adaptively during planning and according to the problem to be solved.

Team AnnieWAY’s Autonomous System for the DARPA Urban Challenge 2007

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner which delivers both range and reflectivity measurements. While range measurements are used to provide 3D scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a concurrent hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition.

Using case-based reasoning for autonomous vehicle guidance

Vehicle guidance in complex scenarios such as inner-city traffic requires an in-depth understanding of the current situation. In order to select the appropriate behavior for an autonomous vehicle, an analysis of the situation is needed. The analysis consists of an estimation of the situations development with respect to the selected behavior. This can only be done using higher-level reasoning techniques. In this paper, an approach for situation interpretation for autonomous vehicles is presented. The approach relies on case-based reasoning in order to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

Design of the planner of team AnnieWAY’s autonomous vehicle used in the DARPA Urban Challenge 2007

This paper reports on the behaviour decision and execution unit of AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the DARPA Urban Challenge 2007 competition. Starting with a short description of the car and its major hardware components, we outline the underlying software structure and focus on the design of the behavior decision module. The selection of maneuvers necessary to accomplish the mission is conducted online via a concurrent hierarchical state machine that specifically ascertains behavior in accordance with California traffic rules. The states and transitions used to model a adequate behaviour are described. We conclude with a report of the results achieved during the competition.