A Diffusion-Model of Joint Interactive Navigation
- URL: http://arxiv.org/abs/2309.12508v2
- Date: Tue, 24 Oct 2023 18:41:26 GMT
- Title: A Diffusion-Model of Joint Interactive Navigation
- Authors: Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu, Vasileios
Lioutas, Justice Sefas, Xiaoxuan Liang, Dylan Green, Setareh Dabiri, Berend
Zwartsenberg, Adam Scibior, Frank Wood
- Abstract summary: We present DJINN - a diffusion based method of generating traffic scenarios.
Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future.
We show how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions.
- Score: 14.689298253430568
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Simulation of autonomous vehicle systems requires that simulated traffic
participants exhibit diverse and realistic behaviors. The use of prerecorded
real-world traffic scenarios in simulation ensures realism but the rarity of
safety critical events makes large scale collection of driving scenarios
expensive. In this paper, we present DJINN - a diffusion based method of
generating traffic scenarios. Our approach jointly diffuses the trajectories of
all agents, conditioned on a flexible set of state observations from the past,
present, or future. On popular trajectory forecasting datasets, we report state
of the art performance on joint trajectory metrics. In addition, we demonstrate
how DJINN flexibly enables direct test-time sampling from a variety of valuable
conditional distributions including goal-based sampling, behavior-class
sampling, and scenario editing.
Related papers
- Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction [0.6202955567445396]
We present a novel trajectory prediction model for autonomous driving.
Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions.
The proposed model showcases strong potential for application in real-world autonomous driving systems.
arXiv Detail & Related papers (2024-11-25T15:03:44Z) - Diffusion-Based Environment-Aware Trajectory Prediction [3.1406146587437904]
The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles.
In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed.
The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data.
arXiv Detail & Related papers (2024-03-18T10:35:15Z) - Controllable Diverse Sampling for Diffusion Based Motion Behavior
Forecasting [11.106812447960186]
We introduce a novel trajectory generator named Controllable Diffusion Trajectory (CDT)
CDT integrates information and social interactions into a Transformer-based conditional denoising diffusion model to guide the prediction of future trajectories.
To ensure multimodality, we incorporate behavioral tokens to direct the trajectory's modes, such as going straight, turning right or left.
arXiv Detail & Related papers (2024-02-06T13:16:54Z) - SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework.
Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations.
We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
arXiv Detail & Related papers (2023-12-31T04:14:43Z) - TrafficBots: Towards World Models for Autonomous Driving Simulation and
Motion Prediction [149.5716746789134]
We show data-driven traffic simulation can be formulated as a world model.
We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving.
Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
arXiv Detail & Related papers (2023-03-07T18:28:41Z) - TrajGen: Generating Realistic and Diverse Trajectories with Reactive and
Feasible Agent Behaviors for Autonomous Driving [19.06020265777298]
Existing simulators rely on system-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios.
We propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic behaviors directly from human demonstration.
In addition, we develop a data-driven simulator I-Sim that can be used to train reinforcement learning models in parallel based on naturalistic driving data.
arXiv Detail & Related papers (2022-03-31T04:48:29Z) - Generating and Characterizing Scenarios for Safety Testing of Autonomous
Vehicles [86.9067793493874]
We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator.
We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project.
We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
arXiv Detail & Related papers (2021-03-12T17:00:23Z) - TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation.
In particular, we leverage an implicit latent variable model to parameterize a joint actor policy.
We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
arXiv Detail & Related papers (2021-01-17T00:29:30Z) - 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) - Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [78.74510891099395]
In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data.
We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene.
arXiv Detail & Related papers (2020-07-23T14:31:25Z)
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.