Group Leader: Dr.-Ing. Maximilian Naumann
Motion planning is key to every automated physical system, and a well investigated subject in the field of robotics. In the field of automated vehicles, the task is to determine an appropriate behavior, resulting in a trajectory, i.e. the state of the vehicle as a function of time. This task is referred to as decision making or motion planning for automated vehicles. The decision is based on previously acquired knowledge about the environment, such as the drivable area and detected objects, but also traffic rules.
The key challenges in motion planning for automated driving arise from the collision risk with other traffic participants. Since human lives are at risk, safety takes the top priority. On the other hand, over-cautious behavior, sometimes referred to as “driving like a learner”, is not only inconvenient but can also cause dangerous situations, as this behavior might cause misunderstanding by humans or entice them to risky overtaking maneuvers, for example.
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms are implementing tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives.
Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable, but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation.
Contact: M.Sc. Piotr Orzechowski
Realistic Simulation for Motion Planning and Decision-Making
Testing on real vehicles is a expensive and, sometimes, risky task. For that reason, we perform a deep study on state of the art simulators, in a way that new planning approaches can be tested. We also focus on finding corner case scenarios that are rarely seen using on-road data.
Contact: Dr. Eduardo José Molinos Vicente
Optimization Based Trajectory Planning
In autonomous robots (and vehicles), trajectory planning is an important task. Once a computed global path/task/behavior is obtained, the trajectory planning will modify them to avoid unexpected obstacles and fulfill the requirements of our vehicle.
In our approach, optimization based methods are used to maximize comfort and safety inside the vehicle. This solutions are tested in simulated and real scenarios.
Contact: Dr. Eduardo José Molinos Vicente
Verifiable Safety in Motion Planning and Decision-Making
Since human lives must never be put at risk, the safety of automated vehicles must be ensured before their on-road deployment. As shown in many exemplary calculations, a validation solely via test drives is not expedient. In order to validate the safety of automated vehicles, formal methods are promising. Here, we focus on analyzing and extending existing concepts such as Responsibility Sensitive Safety (RSS) by Intel/Mobileye and set-based methods as proposed by Matthias Althoff et al. Further, we focus on motion planning and decision making that is safe, yet not over-cautious, since over-cautious behavior might entice human drivers to risky maneuvers (such as overtaking) and thus jeopardize the safety concept.
Realistic Single-Shot and Long-Term Collision Risk for Human-Style Safer Driving
Navigation in congested environments is a challenge for autonomous vehicles. In order to drive in a safer way, the potential collision risk with other obstacles should be considered in the planning algorithm. We focus on assessing realistic collision risk with other traffic participants, to finally enable generating a more human-style and safer driving behavior. In the demonstration videos, the ego vehicle always try to drive to the speed limit and follow the centerline of the road, but should also pay attention to the static and dynamic obstacles that result in certain collision risk to the ego vehicle. In order to reduce the collision risk, the ego vehicle reduces the speed or move laterally slightly to avoid passing the obstacles with low distance and high relative speed. After that, it should retrieve its origin lateral position and velocity. Future work will be done in learning how human drivers balance risk, utility and comfort from the real data. By applying machine learning techniques on recorded human trajectories, a realistic risk balanced driving behavior will be achieved.
Contact: M.Sc. Lingguang Wang
Cooperative Multi-Vehicle Motion Planning
Automated driving has the potential to greatly improve traffic efficiency and safety. This improvement becomes even more significant if not only a single automated vehicle is driving on the road by itself but multiple together.
The ability to communicate and react to each other much quicker and more precise than humans ever could enables the possibility of cooperative, coordinated maneuvers among automated vehicles. In our work, we investigate the potential of multi-vehicle motion planning.
Shown is a comparison between a traditional (non-cooperative) motion planning approach and one based on cooperative multi-vehicle planning.
By planning the trajectories jointly, a coordinated maneuver can be performed, and an overtaking can be performed much more efficiently.
Contact: M.Sc. Christoph Burger
Cooperative Motion Planning in Mixed Traffic
Generating human-like motion in cooperative scenarios with human-driven vehicles is a challenging task. One of the major issues is that most techniques treat the prediction of surrounding vehicles and motion planning as two separate tasks. This way, interactions among traffic participants are neglected. Our work's focus is to explicitly consider these interactions, treating prediction and planning as a joint task and thereby creating cooperative motion plans.
Decision Making under Uncertainty
In real traffic it is necessary to make decisions under partial information. In particular, the intention of other traffic participants in important for planning. A principal framework for modelling such problems are partially observable Markov decision processes (POMDPs), which allow to optimally solve stochastic planning problems. POMDPs are usually solved approximately due to the high problem complexity, e.g. with Monte Carlo Tree Search. We investigate the application of POMDPs for modelling real traffic situations and efficient POMDP solvers.
Contact: M.Sc. Johannes Fischer
Probabilistic Motion Planning for Automated Vehicles
As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered.
Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered.
Thus, we focus on motion planning approaches are targeted towards the different predominant uncertainties in different scenarios, while operating in a continuous action space and ensuring safety.
Contact: Dr.-Ing. Maximilian Naumann
Inverse Reinforcement Learning from Human Behavior
Many planning problems are formulated as the minimization of a cost function which accounts for different criteria such as comfort, safety and efficiency. As an alternative to manually tuning the parameters of the cost function, they can be learned from demonstrations of a human expert using Inverse Reinforcement Learning. We investigate how demonstrated behavior in highway driving can be reproduced by the learned cost function.
Furthermore, we created the Interaction Dataset, a dataset that contains human driving, cycling and walking behavior, along with high-definition lanelet2 maps. We now focus on analyzing this behavior using Inverse Reinforcement Learning, in order to better understand and predict human behavior, but also to generate human-like behavior.
Safe and Risk-Aware Reinforcement Learning for Automated Driving under Uncertainty
Reinforcement learning (RL) helps to learn policies which can predict the current situation and generate long term optimal actions based on that. Applications of RL policies in safety critical domains like automated driving with perception uncertainties are always challenging. For that, we design safety verification layers which guarantee safety of the generated actions from RL and make sure that they are fail-safe. By providing appropriate rewarding scheme, we aim to learn policies that are always safe and still generate long term optimal behaviors.
Contact: M.Sc. Danial Kamran