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 Cram\'er-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.

翻译：集成感知与通信（ISAC）技术能够为联网自动驾驶车辆同时实现定位、环境感知与数据交换。然而，现有大多数ISAC设计优先考虑感知精度与通信吞吐量，对所有目标进行均等处理，忽视了关键障碍物对运动效率的影响。为克服这一局限，本文提出一种面向路径规划的ISAC（PISAC）框架，通过降低规划瓶颈障碍物的感知不确定性，扩展自车的安全可通行路径，从而弥合物理层优化与运动层级规划之间的鸿沟。PISAC的核心在于基于克拉美-罗界与占据膨胀原理，推导出将ISAC发射功率与感知不确定性显式关联的闭式安全边界。基于该模型，我们构建了双层功率分配与运动规划（PAMP）问题，其中内层优化ISAC波束功率分配，外层在考虑不确定性的安全约束下计算无碰撞轨迹。在高保真城市驾驶环境中的综合仿真表明，相较于现有基于ISAC及面向通信的基准方法，PISAC能实现高达40%的成功率提升与超过5%的通行时间缩短，验证了其在提升安全性与效率方面的有效性。