9,447 research outputs found
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
Trajectory planning at high velocities and at the handling limits is a
challenging task. In order to cope with the requirements of a race scenario, we
propose a far-sighted two step, multi-layered graph-based trajectory planner,
capable to run with speeds up to 212~km/h. The planner is designed to generate
an action set of multiple drivable trajectories, allowing an adjacent behavior
planner to pick the most appropriate action for the global state in the scene.
This method serves objectives such as race line tracking, following, stopping,
overtaking and a velocity profile which enables a handling of the vehicle at
the limit of friction. Thereby, it provides a high update rate, a far planning
horizon and solutions to non-convex scenarios. The capabilities of the proposed
method are demonstrated in simulation and on a real race vehicle.Comment: Accepted at The 22nd IEEE International Conference on Intelligent
Transportation Systems, October 27 - 30, 201
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments
Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
Developing an intelligent vehicle which can perform human-like actions
requires the ability to learn basic driving skills from a large amount of
naturalistic driving data. The algorithms will become efficient if we could
decompose the complex driving tasks into motion primitives which represent the
elementary compositions of driving skills. Therefore, the purpose of this paper
is to segment unlabeled trajectory data into a library of motion primitives. By
applying a probabilistic inference based on an iterative
Expectation-Maximization algorithm, our method segments the collected
trajectories while learning a set of motion primitives represented by the
dynamic movement primitives. The proposed method utilizes the mutual
dependencies between the segmentation and representation of motion primitives
and the driving-specific based initial segmentation. By utilizing this mutual
dependency and the initial condition, this paper presents how we can enhance
the performance of both the segmentation and the motion primitive library
establishment. We also evaluate the applicability of the primitive
representation method to imitation learning and motion planning algorithms. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology intelligent vehicle platform. The results show
that the proposed approach can find the proper segmentation and establish the
motion primitive library simultaneously
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Machine learning can provide efficient solutions to the complex problems
encountered in autonomous driving, but ensuring their safety remains a
challenge. A number of authors have attempted to address this issue, but there
are few publicly-available tools to adequately explore the trade-offs between
functionality, scalability, and safety.
We thus present WiseMove, a software framework to investigate safe deep
reinforcement learning in the context of motion planning for autonomous
driving. WiseMove adopts a modular learning architecture that suits our current
research questions and can be adapted to new technologies and new questions. We
present the details of WiseMove, demonstrate its use on a common traffic
scenario, and describe how we use it in our ongoing safe learning research
Search-based Motion Planning for Aggressive Flight in SE(3)
Quadrotors with large thrust-to-weight ratios are able to track aggressive
trajectories with sharp turns and high accelerations. In this work, we develop
a search-based trajectory planning approach that exploits the quadrotor
maneuverability to generate sequences of motion primitives in cluttered
environments. We model the quadrotor body as an ellipsoid and compute its
flight attitude along trajectories in order to check for collisions against
obstacles. The ellipsoid model allows the quadrotor to pass through gaps that
are smaller than its diameter with non-zero pitch or roll angles. Without any
prior information about the location of gaps and associated attitude
constraints, our algorithm is able to find a safe and optimal trajectory that
guides the robot to its goal as fast as possible. To accelerate planning, we
first perform a lower dimensional search and use it as a heuristic to guide the
generation of a final dynamically feasible trajectory. We analyze critical
discretization parameters of motion primitive planning and demonstrate the
feasibility of the generated trajectories in various simulations and real-world
experiments.Comment: 8 pages, submitted to RAL and ICRA 201
Artificial potential functions for highway driving with collision avoidance
We present a set of potential function components to assist an automated or semi-automated vehicle in navigating a multi-lane, populated highway. The resulting potential field is constructed as a superposition of disparate functions for lane- keeping, road-staying, speed preference, and vehicle avoidance and passing. The construction of the vehicle avoidance potential is of primary importance, incorporating the structure and protocol of laned highway driving. Particularly, the shape and dimensions of the potential field behind each obstacle vehicle can appropriately encourage control vehicle slowing and/or passing, depending on the cars' velocities and surrounding traffic. Hard barriers on roadway edges and soft boundaries between navigable lanes keep the vehicle on the highway, with a preference to travel in a lane center
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