Protection level optimization method of ARAIM algorithm for urban road safety

Safety-critical intelligent transportation systems (ITS) applications are surging in recent years. These kinds of applications not only have the accuracy but also the integrity of global navigation satellite system (GNSS) positioning services. In the field of aviation with an open environment, advanced receiver autonomous integrity monitoring (ARAIM) has been widely concerned as a low-cost, highly autonomous integrity monitoring method. However, there are still gaps in the application of urban environments. Moreover, the probability of integrity risk and continuity risk of traditional ARAIM algorithm which is applied for aviation applications in the open environment has been equally allocated, resulting in the relatively conservative protection level. In order to solve the above problems, this paper proposes a protection-level optimization method based on Teaching-learning-based optimization (TLBO), which can realize the reasonable allocation of integrity risk and continuity risk under the integrity requirements of urban road safety, so as to improve the availability of multi-constellation ARAIM. The on-board measured data shows that under the global position system（GPS） + Galileo satellite navigation system (GAL) dual constellation scenario, the average optimization rates of horizontal protection level (HPL) and vertical protection level (VPL) are 50.58% and 44.14%, and the availability of ARAIM for the 10-meter alert limit (AL) is increased by 51.29%. In the GPS+GAL+BDS multi-constellation scenario, the average optimization rates of HPL and VPL are 59.59% and 56.33%, and the availability of ARAIM for the 10-meter AL is improved by 99.29%.