Integrity for autonomous vehicles and towards a novel alert limit determination method

Abstract

Integrity is one critical performance indicator for navigation in safety-critical applications such as autonomous vehicles. Alert limit is one of the representative parameters in integrity monitoring which defines the maximum tolerable positioning error for an operation to safely proceed. But the integrity requirements for global navigation satellite system (GNSS) assessment are quite different from those for other applications. For autonomous vehicles, a reasonable alert limit needs to ensure the vehicle security and take full advantage of the space between vehicle and lane as much as possible. Based on the analysis of integrity application differences from civil aviation to autonomous vehicles, an improved alert limit determination method is proposed in this paper. The kinematic model is firstly introduced into the online determination of alert limit. The integrity risk on two sides are allocated optimally respect to the road geometry and kinematic model. The fixed cuboid bounding box is replaced by a subversive fan-shaped bounding box which is more reasonable to cover the safety-critical areas. The discussion compared with the Ford model also verified the superiority of the proposed method. Finally the paper also gives the alert limits calculated based on the Chinese standards and hopefully it could provide some references for the navigation integrity assessment for autonomous vehicles.

Keywords

Integrity autonomous vehicles alert limit navigation

Get full access to this article

View all access options for this article.

References

Kassas

Closas

Gross

Navigation systems panel report navigation systems for autonomous and semi-autonomous vehicles: current trends and future challenges. IEEE Aerosp Electron Syst Mag 2019; 34(5): 82–84.

SAE International. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016B). 2018.

Koopman

Wagner

Autonomous vehicle safety: an interdisciplinary challenge. IEEE Intell Transp Syst Mag 2017; 9(1): 90–96.

Jiao

Yang

Jiang

, et al. Real-time lane detection and tracking for autonomous vehicle applications. Proc Inst Mech Eng D J Automob Eng 2019; 233(9): 2301–2311.

Zhang

Reliable integrated navigation system based on adaptive fuzzy federated Kalman filter for automated vehicles. Proc Inst Mech Eng D J Automob Eng 2010; 224(3): 327–346.

Meng

Liu

Zeng

, et al. Neumann-Hoffman code evasion and stripping method for BeiDou software-defined receiver. J Navig 2017; 70(1): 101–119.

Venkita

Willemsen

Alirezaei

, et al. Switching from autopilot to the driver: a transient performance analysis. Proc Inst Mech Eng D J Automob Eng 2019; 234(5): 1346–1360.

Chen

Chan

A reliable multisensor fusion strategy for land vehicle positioning using low-cost sensors. Proc Inst Mech Eng D J Automob Eng 2014; 228(12): 1375–1397.

Walter

Enge

Blanch

, et al. Worldwide vertical guidance of aircraft based on modernized GPS and new integrity augmentations. Proc IEEE 2008; 96(12): 1918–1935.

10.

Meng

Liu

Zeng

, et al. Improved ARAIM fault modes determination scheme based on feedback structure with probability accumulation. GPS Solut 2019; 23(1): 16.

11.

Joerger

Spenko

Towards navigation safety for autonomous cars. Inside GNSS 2017; November/December: 40–48.

12.

Changalvala

Hafiz

LiDAR data integrity verification for autonomous vehicle. IEEE Access 2019; 7: 138018–138031.

13.

Chen

Luo

, et al. Safety benefit evaluation of intelligent driving systems based on multisource data mining. Proc Inst Mech Eng D J Automob Eng 2019; 233(9): 2362–2370.

14.

Reid

Houts

Cammarata

, et al. Localization requirements for autonomous vehicles. SAE Int J Connect Autom Veh 2019; 2: 173–190.

15.

US Department of Transportation. Federal Radionavigation Plan. 2017, https://www.transportation.gov/pnt/radionavigation-systems-planning

16.

Zhu

Marais

Bétaille

, et al. GNSS position integrity in urban environments: a review of literature. IEEE Trans Intell Transp Syst 2018; 19(9): 2762–2778.

17.

Yang

Practical method for 4-dimentional strategic air traffic management problem with convective weather uncertainty. IEEE Trans Intell Transp Syst 2017; 19(6): 1697–1708.

18.

Abou-zeid

Pervez

Adinoyi

, et al. Cellular V2X transmission for connected and autonomous vehicles standardization, applications, and enabling technologies. IEEE Consum Electron Mag 2019; 8(6): 91–98.

19.

Takefuji

Connected vehicle security vulnerabilities. IEEE Technol Society Mag 2018; 37(1): 15–18.

20.

Rosique

Navarro

Fernández

, et al. A systematic review of perception system and simulators for autonomous vehicles research. Sensors 2019; 19(3): 648.

21.

Meng

Liu

Zeng

, et al. An efficient BeiDou DBZP-based weak signal acquisition scheme for software-defined receiver. IET Radar Sonar Navig 2018; 12(6): 654–662.

22.

Kuutti

Fallah

Katsaros

, et al. A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet Things J 2018; 5(2): 829–846.

23.

Hobert

Festag

Llatser

, et al. Enhancements of V2X communication in support of cooperative autonomous driving. IEEE Commun Mag 2015; 53(12): 64–70.

24.

Loureiro

Benmoussa

Touati

, et al. Integration of fault diagnosis and fault-tolerant control for health monitoring of a class of MIMO intelligent autonomous vehicles. IEEE Trans Veh Technol 2014; 63(1): 30–39.

25.

Al Hage

El Najjar

Pomorski

. Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler divergence: application on collaborative multi-robot system. Inf Fusion 2017; 37: 61–76.

26.

Washington State Department of Transportation. Design manual (M 22-01.19). Olympia, WA, 2020. https://wsdot.wa.gov/Publications/Manuals/M22-01.htm

27.

Ministry of Transport of the People’s Republic of China. Design specification for highway alignment (JTG D20-2017). Beijing, China: Communications Press, 2018.