  Autonomous driving has long grappled with the need for precise absolute
localization, making full autonomy elusive and raising the capital entry
barriers for startups. This study delves into the feasibility of local
trajectory planning for level-2+ (L2+) semi-autonomous vehicles without the
dependence on accurate absolute localization. Instead, we emphasize the
estimation of the pose change between consecutive planning frames from motion
sensors and integration of relative locations of traffic objects to the local
planning problem under the ego car's local coordinate system, therefore
eliminating the need for an absolute localization. Without the availability of
absolute localization for correction, the measurement errors of speed and yaw
rate greatly affect the estimation accuracy of the relative pose change between
frames. We proved that the feasibility/stability of the continuous planning
problem under such motion sensor errors can be guaranteed at certain defined
conditions. This was achieved by formulating it as a Lyapunov-stability
analysis problem. Moreover, a simulation pipeline was developed to further
validate the proposed local planning method. Simulations were conducted at two
traffic scenes with different error settings for speed and yaw rate
measurements. The results substantiate the proposed framework's functionality
even under relatively inferior sensor errors. We also experiment the stability
limits of the planned results under abnormally larger motion sensor errors. The
results provide a good match to the previous theoretical analysis. Our findings
suggested that precise absolute localization may not be the sole path to
achieving reliable trajectory planning, eliminating the necessity for
high-accuracy dual-antenna GPS as well as the high-fidelity maps for SLAM
localization.
