Communication-Critical Planning via Multi-Agent Trajectory Exchange
- URL: http://arxiv.org/abs/2303.06080v1
- Date: Fri, 10 Mar 2023 16:59:24 GMT
- Title: Communication-Critical Planning via Multi-Agent Trajectory Exchange
- Authors: Nathaniel Moore Glaser, Zsolt Kira
- Abstract summary: This paper addresses the task of joint multi-agent perception and planning.
It relates to the real-world challenge of collision-free navigation for connected self-driving vehicles.
- Score: 21.923724399511798
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper addresses the task of joint multi-agent perception and planning,
especially as it relates to the real-world challenge of collision-free
navigation for connected self-driving vehicles. For this task, several
communication-enabled vehicles must navigate through a busy intersection while
avoiding collisions with each other and with obstacles. To this end, this paper
proposes a learnable costmap-based planning mechanism, given raw perceptual
data, that is (1) distributed, (2) uncertainty-aware, and (3)
bandwidth-efficient. Our method produces a costmap and uncertainty-aware
entropy map to sort and fuse candidate trajectories as evaluated across
multiple-agents. The proposed method demonstrates several favorable performance
trends on a suite of open-source overhead datasets as well as within a novel
communication-critical simulator. It produces accurate semantic occupancy
forecasts as an intermediate perception output, attaining a 72.5% average
pixel-wise classification accuracy. By selecting the top trajectory, the
multi-agent method scales well with the number of agents, reducing the hard
collision rate by up to 57% with eight agents compared to the single-agent
version.
Related papers
- SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for
Autonomous Vehicle Driving [2.7195102129095003]
We propose a semantic-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture semantics along with spatial information.
Specifically, we achieve this by implementing a semantic-aware selection of relevant agents from the scene and passing them through an attention mechanism.
Our results show that the proposed approach outperforms state-of-the-art baselines and provides more accurate and scene-consistent predictions.
arXiv Detail & Related papers (2023-06-26T17:54:24Z) - iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed
Multi-Agent Reinforcement Learning [57.24340061741223]
We introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios.
Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
arXiv Detail & Related papers (2023-06-09T20:12:02Z) - Traj-MAE: Masked Autoencoders for Trajectory Prediction [69.7885837428344]
Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers.
We propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment.
Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods.
arXiv Detail & Related papers (2023-03-12T16:23:27Z) - Learning to Communicate and Correct Pose Errors [75.03747122616605]
We study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner.
We propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and to reach a consensus about those errors.
arXiv Detail & Related papers (2020-11-10T18:19:40Z) - Multimodal Trajectory Prediction via Topological Invariance for
Navigation at Uncontrolled Intersections [45.508973373913946]
We focus on decentralized navigation among multiple non-communicating rational agents at street intersections without traffic signs or signals.
Our key insight is that the geometric structure of the intersection and the incentive of agents to move efficiently and avoid collisions (rationality) reduces the space of likely behaviors.
We design Multiple Topologies Prediction (MTP), a data-driven trajectory-prediction mechanism that reconstructs trajectory representations of high-likelihood modes in multiagent intersection scenes.
arXiv Detail & Related papers (2020-11-08T02:56:42Z) - A Combination of Theta*, ORCA and Push and Rotate for Multi-agent
Navigation [0.0]
We study the problem of multi-agent navigation in static environments when no centralized controller is present.
Each agent is controlled individually and relies on three algorithmic components to achieve its goal.
arXiv Detail & Related papers (2020-08-03T22:22:43Z) - SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
We propose advances that address two key challenges in future trajectory prediction.
multimodality in both training data and predictions and constant time inference regardless of number of agents.
arXiv Detail & Related papers (2020-07-26T08:17:10Z) - Traffic Agent Trajectory Prediction Using Social Convolution and
Attention Mechanism [57.68557165836806]
We propose a model to predict the trajectories of target agents around an autonomous vehicle.
We encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents.
To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease.
arXiv Detail & Related papers (2020-07-06T03:48:08Z) - MultiXNet: Multiclass Multistage Multimodal Motion Prediction [27.046311751308775]
MultiXNet is an end-to-end approach for detection and motion prediction based directly on lidar sensor data.
The method was evaluated on large-scale, real-world data collected by a fleet of SDVs in several cities.
arXiv Detail & Related papers (2020-06-03T01:01:48Z) - Implicit Multiagent Coordination at Unsignalized Intersections via
Multimodal Inference Enabled by Topological Braids [15.024091680310109]
We focus on navigation among rational, non-communicating agents at unsignalized street intersections.
We represent modes of joint behavior in a compact and interpretable fashion using the formalism of topological braids.
We design a decentralized planning algorithm that generates actions aimed at reducing the uncertainty over the mode of the emerging multiagent behavior.
arXiv Detail & Related papers (2020-04-10T19:01:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.