Classification of driver intentions at roundabouts - Sackmann, Moritz; Bey, Henrik; Hofmann, Ulrich; Thielecke, Jörn (VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems, 2020)
Classification of other drivers' intentions is an important requirement for automated driving. We present two methods to estimate whether a driver leaves a roundabout. The first, like many other approaches to this problem, requires training data of the specific roundabout to extract typical behavior patterns. Afterwards, these patterns are used for classification of other drivers' intentions. The second approach generates typical behavior patterns from a precise map. Consequently, no training data is required and classification can be performed on arbitrary roundabouts as long as a map is available. Experimental evaluation on a real world dataset of 266 trajectories shows that the performance of the map-based approach is comparable to the data-driven approach. The classification result can be used in a later stage for behavior planning of automated vehicles or driver assistance systems.
CRIS-Team Scopus Importer:2020-09-25
Keywords: Automated Driving; Intention Classification; Pattern Recognition; Trajectory Analysis

Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling - Leemann, Tobias; Sackmann, Moritz; Thielecke, Jörn; Hofmann, Ulrich (Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021)
Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast’s uncertainty. The Multiple Hypotheses Prediction (MHP) approach addresses this problem by providing several hypotheses that represent possible outcomes. Unfortunately, with the common l2 loss function, these hypotheses do not preserve the data distribution’s characteristics. We propose an alternative loss for distribution preserving MHP and review relevant theorems supporting our claims. Furthermore, we empirically show that our approach yields more representative hypotheses on a synthetic and a real-world motion prediction data set. The outputs of the proposed method can directly be used in sampling-based Monte-Carlo method
Keywords: Machine Learning; Neural Networks; Uncertainty Modeling; Multi Hypotheses Prediction

Frenet Coordinate Based Driving Maneuver Prediction at Roundabouts Using LSTM Networks - Vogl, Carina; Sackmann, Moritz; Kürzinger, Ludwig; Hofmann, Ulrich (Proceedings - CSCS 2020: ACM Computer Science in Cars Symposium, 2020)
Driving maneuver prediction is a key requirement for automated vehicles to assess situations and effectively navigate in urban environments. In this paper, we present three models to predict whether a vehicle leaves a roundabout at a specific exit. We develop a Feedforward neural network (FNN), as well as two Long short-Term memory (LSTM) networks for this task. We propose several concepts that generalize the models to roundabouts with different radii, layouts, and numbers of exits. For this purpose, we also introduce Frenet coordinates with circles as reference paths. We evaluate our models based on the binary cross-entropy loss and the distance to the exit at which a reliable prediction is obtained in a leave-one-out cross-validation fashion, where one exit is always entirely used as the test set. Training and evaluation is performed on a data set of nearly 4,000 trajectories that we captured using a drone. Our best model achieves a reliable prediction on average 9.34m before an exit for class "Leaving"and 8.13m before an exit for class "Staying".
CRIS-Team Scopus Importer:2021-04-19
Keywords: Automated Driving; Frenet Coordinates; Intention Classification; LSTM Networks; Maneuver Prediction

Handling Prediction Model Errors in Planning for Automated Driving Using POMDPs - Bey, Henrik; Sackmann, Moritz; Lange, Alexander; Thielecke, Jörn (2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021)
Decision making under uncertainty is a key challenge in automated driving. A source of uncertainty is the unknown driving behavior of other traffic participants surrounding the automated vehicle. To take this uncertainty into account, we formulate the planning problem as a Partially Observable Markov Decision Process (POMDP) and solve it with the state-of-the-art sampling-based POMCP(OW) solver. Within planning, a derivative of the Intelligent Driver Model (IDM) is used to predict other traffic participants. For evaluation, the POMDP planner runs in a closed-loop simulation environment where the other traffic participants are controlled by machine-learned driver models. The different models used in planning and simulation constitute a true prediction model error. Compared to a classical search-based planner (and reactive driver models), the POMDP leads to less critical situations while retaining the comfort and efficiency of foresighted planning. Both planning approaches apply a particle filter to estimate others' driving characteristics-but only the POMDP approach is able to consider the whole range of possible characteristics. Several variants of the POMDP solver were evaluated, allowing for different weighting of safety vs. comfort/efficiency.
CRIS-Team Scopus Importer:2021-11-12
Keywords: Conferences; Decision making; Markov processes; Particle filters; Planning; Predictive models; Uncertainty

Learning a Diverse and Cooperative Policy for Predicting Roundabout Traffic Situations - Sackmann, Moritz; Bey, Henrik; Hofmann, Ulrich; Thielecke, Jörn (14. Uni-DAS e.V. Workshop Fahrerassistenz und automatisiertes Fahren, 2022)
Keywords: Automated Driving; Reinforcement Learning; Behavior Prediction

Modeling Driver Behavior using Adversarial Inverse Reinforcement Learning - Sackmann, Moritz; Bey, Henrik; Hofmann, Ulrich; Thielecke, Jörn (32rd IEEE Intelligent Vehicles Symposium, 2022)
Keywords: Automated Driving; Reinforcement Learning; Inverse Reinforcement Learning; Behavior Modeling; Trajectory Prediction

Multi-Step Training for Predicting Roundabout Traffic Situations - Sackmann, Moritz; Leemann, Tobias; Bey, Henrik; Hofmann, Ulrich; Thielecke, Jörn (IEEE International Intelligent Transportation Systems Conference (ITSC), 2021)
Predicting the future trajectories of surrounding vehicles is an important challenge in automated driving, especially in highly interactive environments such as roundabouts. Many works approach the task with behavioral cloning: A single-step prediction model is established by learning the mapping of states to the corresponding actions from a fixed dataset. To achieve a long term trajectory prediction, the single-step model is repeatedly executed. However, models learned with the behavioral cloning approach are unable to compensate for the accumulating errors that inevitably arise after repeated execution. Instead, we propose the application of multi-step learning, which directly minimizes the long term prediction error by recursively executing the model during training. This leads to a more robust and precise prediction model. The idea is showcased on a real-world dataset of more than 1000 trajectories at two roundabouts.
CRIS-Team Scopus Importer:2021-11-12
Keywords: Cloning; Conferences; Intelligent transportation systems; Predictive models; Task analysis; Training; Trajectory

Parameter Sharing Reinforcement Learning for Modeling Multi-Agent Driving Behavior in Roundabout Scenarios - Konstantinidis, Fabian; Sackmann, Moritz; De Candido, Oliver; Hofmann, Ulrich; Thielecke, Jörn; Utschick, Wolfgang (IEEE International Intelligent Transportation Systems Conference (ITSC), 2021)
Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts, is a challenging task. We address this task using a Multi-Agent Reinforcement Learning (MARL) approach that learns a driving policy based on a minimal set of assumptions: drivers want to move forward and avoid collisions while maintaining low accelerations. Each agent's actions depend only on his observation of the local environment; no explicit communication between agents is possible. In order to teach the agents to safely interact with each other, and for example, respect right-of-way rules, we use parameter sharing: During training all vehicles are controlled by the same policy and the aggregated experiences are used to improve the policy. Moreover, parameter sharing enables us to use the efficient Soft Actor Critic (SAC) algorithm for training. The approach is evaluated in a roundabout setting with different traffic densities. Furthermore, the ability of the model to generalize is assessed in an untrained roundabout. In both settings, success rates above 97 % demonstrate that a safe and transferable policy is learned.
CRIS-Team Scopus Importer:2021-11-12
Keywords: Network architecture; Recurrent neural networks; Reinforcement learning; Roads; Systematics; Training; Trajectory

POMDP Planning at Roundabouts - Bey, Henrik; Sackmann, Moritz; Lange, Alexander; Thielecke, Jörn (2021 IEEE Intelligent Vehicles Symposium Workshops, 2021)
CRIS-Team WoS Importer:2022-04-29

Prediction Error Reduction of Neural Networks for Car-Following Using Multi-Step Training - Sackmann, Moritz; Bey, Henrik; Hofmann, Ulrich; Thielecke, Jörn (2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020, 2020)
Predicting the surrounding vehicles' behavior is an important requirement for automated driving as it enables estimating others' reactions to the own behavior during planning as well as the identification of critical situations. This work proposes a recursive multi-step training scheme for neural networks that predict other vehicles' positions in a highway car-following scenario. We implement a neural network and compare the proposed approach to the commonly used singlestep training as well as parametric models. For this, the Intelligent Driver Model (IDM) and its derivatives have been calibrated using the same approach. Evaluation is performed on 10 hours of real-world car-following situations, extracted from the extensive HighD dataset. Given equal inputs, we show that a minimal neural network with two layers composed of three neurons each surpasses the prediction performance of both the parametric prediction models and the network trained with the standard single-step approach.
CRIS-Team Scopus Importer:2021-04-19

Tutorial on sampling-based POMDP-planning for automated driving - Bey, Henrik; Tratz, Maximilian; Sackmann, Moritz; Lange, Alexander; Thielecke, Jörn (VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems, 2020)
Behavior planning of automated vehicles entails many uncertainties. Partially Observable Markov Decision Processes (POMDP) are a mathematical framework suited for formulating the arising sequential decision problems. Solving POMDPs used to be intractable except for overly simplified examples, especially when execution time is of importance. Recent sampling-based solvers alleviated this problem by searching not for the exact but rather an approximated solution, and made POMDPs usable for many real-world applications. One of these algorithms is the Adaptive Belief Tree (ABT) algorithm which will be analyzed in this work. The scenario under consideration is an uncertain obstacle in the way of an automated vehicle. Following this example, the setup of POMDP and ABT is derived and the impact of important parameters is assessed in simulation. As such, this work provides a hands-on tutorial, giving insights and hints on how to overcome the pitfalls in using sampling-based POMDP solvers.
CRIS-Team Scopus Importer:2020-09-25
Keywords: Automated Driving; Decision Making under Uncertainty; POMDP