Alapfogalmak
REDEFINED proposes a novel real-time motion planning algorithm using neural networks for safe trajectory design.
Kivonat
The article introduces REDEFINED, a receding-horizon motion planning algorithm that leverages neural networks for safe trajectory design in complex environments. It addresses challenges in real-time planning and collision avoidance, outperforming state-of-the-art methods. The content covers theoretical formulations, zonotope representations, exact signed distance computation, neural network implementation, and online operation details.
Introduction to Autonomous Vehicles
Autonomous vehicles aim to reduce accidents and collisions.
Real-time trajectory planning is crucial for safe navigation.
Challenges include dynamic obstacles and receding-horizon planning.
Offline Reachability Analysis
Zonotope-based reachable sets approximate vehicle behavior.
Overcoming challenges of nonlinear dynamics in trajectory design.
Simplifying dynamics and pre-specifying maneuvers for safety.
Exact Signed Distance Computation
Theorem on computing signed distance between zonotopes exactly.
Neural network architecture using ReLU functions for distance calculation.
Closed-form expression for signed distance between collections of zonotopes.
Online Operation Algorithm
Algorithm detailing the online planning process of REDEFINED.
Sensing obstacles, optimizing trajectories, and predicting vehicle states.
Ensuring not-at-fault behavior through iterative planning iterations.
Experimental Setup
Implementation details using PyTorch and IPOPT for optimization.
Simulation environment with highway driving scenarios and moving obstacles.
Batched optimization problem formulation for efficient trajectory synthesis.
Results Comparison
Mean constraint evaluation time comparison between REFINE and REDEFINED.
Evaluation of constraint speed and gradient computation efficiency.
Statisztikák
REDEFINED uses offline reachability analysis to compute zonotope-based reachable sets that overapproximate the motion of the ego vehicle.
Idézetek
"Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging."
"REDEFINED proposes a novel real-time motion planning algorithm named Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions."