JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems

• URL: http://arxiv.org/abs/2606.20686v1
• Date: Sun, 14 Jun 2026 11:15:44 GMT
• Title: JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems
• Abstract summary: This paper presents a joint prediction-planning diffusion framework that treats participant prediction and robot planning as a single conditional trajectory generation problem.<n> Experiments in scenario-grounded simulation, naturalistic pedestrian replay, Isaac Sim validation, and ROS/Orin deployment show that joint sampling improves tail safety and runtime efficiency.
• Score: 1.9236465591431287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shared-space transportation operation requires low-speed autonomous platforms to navigate safely and efficiently among pedestrians, service robots, micromobility users, carts, and other road users. Most existing systems decompose this problem into trajectory prediction followed by motion planning, which creates one-way information flow: predicted participant futures influence the robot plan, but the selected robot plan cannot influence the predicted multi-agent evolution. This paper presents a joint prediction-planning diffusion framework that treats participant prediction and robot planning as a single conditional trajectory generation problem, where the model samples the future robot trajectory and all participant trajectories from one coupled distribution using a causal Transformer with cross-trajectory attention. To replace heuristic repulsive post-processing, the framework introduces differentiable safety potential guidance, a time-varying occupancy-probability potential whose gradient directly guides the joint sampler, and conditional flow matching is used to reduce inference steps while preserving multimodal trajectory diversity. The evaluation emphasizes shared-space operational effects, including near misses, blockage time, induced participant deviation, hard-braking events, and embedded latency, rather than treating average displacement error and final displacement error as the main result. Experiments in scenario-grounded simulation, naturalistic pedestrian replay, Isaac Sim validation, and ROS/Orin deployment show that joint sampling improves tail safety and runtime efficiency over a separated prediction-then-planning baseline.

Related papers

• One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation [4.570068778162319]
  We show that a single pretrained diffusion traffic model can serve two complementary roles in the autonomous driving development loop.<n>On the planning side, we introduce a Single-Stream Dual-Stream (SSDS) diffusion-transformer decoder that fuses scene context via joint attention.<n>We leverage the same diffusion model as a controllable scenario generator to create realistic long-tail driving interactions for closed-loop evaluation.
  arXiv  Detail & Related papers  (2026-09-04T09:22:54Z)
• Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving [46.91409391705346]
  Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving.<n>A fundamental bottleneck in current systems is the disconnect between highly expressive uncertainty modeling and interpretable, safe motion planning.<n>We propose a novel sample-conditioned differentiable planning framework that bridges this gap by explicitly incorporating diffusion-generated future trajectories into the optimization process.
  arXiv  Detail & Related papers  (2026-06-02T08:10:02Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments [19.811711277536187]
  Multi-robot exploration commonly decouples frontier allocation from local navigation.<n>We propose VORL-EXPLORE, a hybrid learning and planning framework that addresses this limitation through execution fidelity.<n>It also drives a risk-aware adaptive arbitration mechanism between global A* guidance and a reactive reinforcement learning policy.
  arXiv  Detail & Related papers  (2026-03-09T05:20:33Z)
• STGDPM:Vessel Trajectory Prediction with Spatio-Temporal Graph Diffusion Probabilistic Model [0.0]
  Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions.<n>Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states.<n>We propose modeling interactions as dynamic graphs, replacing traditional aggregation-based techniques that rely on vessel states.
  arXiv  Detail & Related papers  (2025-03-11T05:50:27Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories.<n>The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking [23.608125748229174]
  We propose a generic generative neural system for multi-agent trajectory prediction involving heterogeneous agents. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2021-02-18T02:25:35Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network [29.289670231364788]
  In this paper, we propose a generic generative neural system for multi-agent trajectory prediction. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2020-02-14T20:11:13Z)

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.