How to integrate GraphBasedLocalTrajectoryPlanner with ROS for autonomous driving?

The planner's open interfaces allow customization for ROS nodes; consult the documentation for guidance, but it may require additional wrapping or adaptation for seamless integration.

What's the performance overhead of the graph-based approach on standard CPUs?

While validated for high-speed racing, the multilayer graph method can be computationally intensive; real-time performance depends on hardware specs and scenario complexity, so benchmarking is advised.

How to tune cost functions for different racing tracks or conditions?

Cost functions are configurable within the planner's parameters; detailed tuning requires diving into the source code and documentation, as there's no GUI or simplified interface provided.

Can this planner handle multi-agent scenarios with other dynamic vehicles?

Yes, it's designed for dynamic scenarios per the research paper, but effective handling requires proper integration with perception systems and behavior planning for obstacle prediction.

GraphBasedLocalTrajectoryPlanner vs. Frenet-based planners: which is better for autonomous racing?

Graph-based planners offer globally optimal solutions across multiple actions, ideal for flexible high-speed racing, while Frenet planners are often simpler for lane-following but less adaptable to complex maneuvers.

GraphBasedLocalTrajectoryPlanner

LGPL-3.0Python

A Python-based local trajectory planner using multilayer graphs for autonomous race vehicles, returning cost-optimal action sets.

GitHub

What is GraphBasedLocalTrajectoryPlanner?

Graph-Based Local Trajectory Planner is a Python-based framework for generating local trajectories for autonomous race vehicles using a multilayer graph approach. It computes globally cost-optimal solutions for driving actions like keeping straight or passing, filtering out infeasible options to ensure safe and efficient planning. The planner has been validated in real-world racing scenarios, achieving speeds over 200kph.

Target Audience

Autonomous vehicle researchers, robotics engineers, and developers working on high-speed motion planning for race vehicles or dynamic driving scenarios.

Value Proposition

It offers a flexible, graph-based approach to trajectory planning with proven real-world performance, providing cost-optimal action sets and tools for integration with custom behavior planners, making it ideal for high-speed autonomous racing applications.

Overview

Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

Use Cases

Best For

High-speed autonomous racing trajectory planning

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264 stars59 forks0 contributors

Dynamic scenario motion planning for vehicles

Research on graph-based algorithms for robotics

Developing behavior-aware local planners

Real-time trajectory optimization in constrained environments

Testing autonomous driving algorithms in race conditions

Not Ideal For

Low-speed urban autonomous driving applications with dense, static obstacles
Projects requiring real-time execution on embedded systems with strict C++ dependencies
Teams needing certified safety-critical software for commercial deployment
Quick prototyping without deep expertise in trajectory planning algorithms

Pros & Cons

Pros

Globally Optimal Actions

Computes cost-optimal trajectories for each driving action like keeping straight or passing, ensuring efficiency in high-speed scenarios as highlighted in the README.

Automatic Feasibility Filtering

Filters out infeasible action primitives from the returned set, enhancing safety by providing only viable options for planning.

Real-World High-Speed Validation

Successfully tested at speeds over 200kph during Roborace, demonstrating proven performance in extreme racing conditions.

Flexible Behavior Integration

Allows action selection via priority lists or custom behavior planners, enabling adaptable decision-making for dynamic scenarios.

Cons

No Safety Certification

Explicitly disclaimed as not safety-validated in the README, making it risky for commercial use without thorough safety assessments.

Computational Intensity

The multilayer graph approach may be resource-heavy, potentially limiting real-time performance on low-power or non-specialized hardware.

Academic Codebase Limitations

As research-oriented software, it may lack polished documentation, long-term support, or ease of integration compared to commercial tools.

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