Energy- and Runtime-Efficient Trajectory Planning via SIMD Vectorization with Precision Reduction

Kurzfassung

Trajectory planning is a critical module in autonomous driving, where real-time capability and accuracy directly impact safety. Owing to the inherent algorithmic complexity, the energy consumption of trajectory planning becomes a non-negligible concern. This paper proposes single instruction, multiple data (SIMD)-based CPU acceleration of trajectory planning, combined with nondimensionalization for precisionaware optimization. The approach analyzes the trade-off between reduced bit-width and planning reliability, introducing a new scaling scheme that optimizes numerical accuracy under reducedprecision computation. Experimental results on the NVIDIA Jetson Orin (Arm Cortex-A78AE with Arm NEON) demonstrate that vectorized trajectory planning with single-precision floatingpoint arithmetic (IEEE 754) achieves a 1.9× speedup and 44% energy reduction compared to scalar double-precision arithmetic, while half-precision floating-point arithmetic fails to ensure reliability in critical scenarios. The implementation is available as open source software at url-after-acceptance.