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Planning Oriented Integrated Sensing and Communication

Xibin Jin, Guoliang Li, Shuai Wang, Fan Liu, Miaowen Wen, Huseyin Arslan, Derrick Wing Kwan Ng, Chengzhong Xu

TL;DR

This work addresses planning efficiency and safety gaps in ISAC for connected autonomous vehicles by introducing Planning Oriented ISAC (PISAC). It derives a CRB-based safety bound and an occupancy-inflation mechanism to translate sensing uncertainties into deterministic planning constraints, enabling a bilevel PAMP formulation that couples ISAC power allocation with motion planning. The inner level uses a convex double-circle approximation (DCA) via CVXPY for power allocation, while the outer level leverages ADMM for planning under uncertainty; the approach is validated in high-fidelity CARLA simulations, showing up to a 40% increase in success rate and a 5% reduction in traversal time over benchmarks, along with balanced sensing/communication performance. By bridging physical-layer ISAC design and horizon-based motion planning, PISAC provides a practical, scalable framework for safe and efficient autonomous driving in cluttered urban environments, with results grounded in CRB-based uncertainty analysis and occupancy inflation.

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.

Planning Oriented Integrated Sensing and Communication

TL;DR

This work addresses planning efficiency and safety gaps in ISAC for connected autonomous vehicles by introducing Planning Oriented ISAC (PISAC). It derives a CRB-based safety bound and an occupancy-inflation mechanism to translate sensing uncertainties into deterministic planning constraints, enabling a bilevel PAMP formulation that couples ISAC power allocation with motion planning. The inner level uses a convex double-circle approximation (DCA) via CVXPY for power allocation, while the outer level leverages ADMM for planning under uncertainty; the approach is validated in high-fidelity CARLA simulations, showing up to a 40% increase in success rate and a 5% reduction in traversal time over benchmarks, along with balanced sensing/communication performance. By bridging physical-layer ISAC design and horizon-based motion planning, PISAC provides a practical, scalable framework for safe and efficient autonomous driving in cluttered urban environments, with results grounded in CRB-based uncertainty analysis and occupancy inflation.

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.
Paper Structure (13 sections, 36 equations, 4 figures, 1 table)

This paper contains 13 sections, 36 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. ISAC Signal Model
  4. Motion Planning Model
  5. PISAC Problem Formulation
  6. Safety Bound Under ISAC Uncertainties
  7. Cramér-Rao Bound
  8. Probability Bound by Occupancy Inflation
  9. Bilevel PAMP Optimization
  10. Power Allocation via DCA
  11. Motion Planning via ADMM
  12. Simulation Results
  13. Conclusion

Figures (4)

  • Figure 1: The CAV system with $1$ EV, $K$ OVs, and $1$ ISAC RSU.
  • Figure 2: Power, trajectory and motion profiles of different schemes, where OVs are indexed by color, and the bbox denotes $\mathbb{O}_k^p$.
  • Figure 3: Pass times and success rates of PISAC and RDA.
  • Figure 4: Rates and CRBs versus SNR.

Theorems & Definitions (2)

  • proof
  • proof