Planning Oriented Integrated Sensing and Communication

2025-10-27

AI Summary

Existing Integrated Sensing and Communication (ISAC) designs prioritize sensing accuracy and communication throughput while neglecting the impact of critical obstacles on motion efficiency, leading to a disconnect between physical-layer optimization and motion planning. Method: This paper proposes a planning-oriented ISAC framework, introducing the novel concept of “planning-critical obstacles” and establishing a closed-form safety boundary model that jointly incorporates the Cramér–Rao bound and occupancy inflation to co-optimize sensing uncertainty and motion planning. It features a two-layer closed-loop design integrating power allocation and motion planning, unifying beamforming, uncertainty-aware modeling, and safety-critical trajectory generation. Contribution/Results: Evaluated in high-fidelity urban driving simulations, the framework achieves a 40% improvement in task success rate and reduces path traversal time by over 5%, significantly enhancing navigation safety and traffic efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Integrated sensing and communication (ISAC) enables simultaneous localization, environment perception, and data exchange for connected autonomous vehicles. However, most existing ISAC designs prioritize sensing accuracy and communication throughput, treating all targets uniformly and overlooking the impact of critical obstacles on motion efficiency. To overcome this limitation, we propose a planning-oriented ISAC (PISAC) framework that reduces the sensing uncertainty of planning-bottleneck obstacles and expands the safe navigable path for the ego-vehicle, thereby bridging the gap between physical-layer optimization and motion-level planning. The core of PISAC lies in deriving a closed-form safety bound that explicitly links ISAC transmit power to sensing uncertainty, based on the Cramér-Rao Bound and occupancy inflation principles. Using this model, we formulate a bilevel power allocation and motion planning (PAMP) problem, where the inner layer optimizes the ISAC beam power distribution and the outer layer computes a collision-free trajectory under uncertainty-aware safety constraints. Comprehensive simulations in high-fidelity urban driving environments demonstrate that PISAC achieves up to 40% higher success rates and over 5% shorter traversal times than existing ISAC-based and communication-oriented benchmarks, validating its effectiveness in enhancing both safety and efficiency.

Problem

• Reduces sensing uncertainty for planning-critical obstacles in autonomous vehicles
• Bridges gap between physical-layer ISAC optimization and motion-level planning
• Enhances safety and efficiency through joint power allocation and trajectory planning

Innovation

• Planning-oriented ISAC framework prioritizes bottleneck obstacles
• Closed-form safety bound links transmit power to uncertainty
• Bilevel optimization allocates power and plans trajectory