Localization Requirements for Autonomous Vehicles

Reid, Tyler; Houts, Sarah E.; Cammarata, R. C.; Mills, Graham A.; Agarwal, Siddharth; Vora, Ankit; Pandey, Gaurav

doi:10.4271/12-02-03-0012

Cited by 156 publications

(108 citation statements)

References 19 publications

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“…Attempts are currently being made to transfer these established concepts to mobile platforms, especially for highly automated driving. For the first time (Reid et al, 2019) specified alert limits for the localization accuracy for autonomous vehicles of 0.1 m (95 %) in position and 0.17 • in orientation. This definition is mainly linked to a typical lane geometry.…”

Section: Current Situationmentioning

confidence: 99%

Integrity – A Topic for Photogrammetry ?

Schön

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Photogrammetric methods and sensors like LIDAR, RADAR and cameras are becoming more and more important for new applications like highly automatic driving, since they enable capturing relative information of the ego vehicle w.r.t its environment. Integrity measure the trust that we can put in the navigation information of a system. The concept of integrity was first developed for civil aviation and is linked to reliability concepts well known in geodesy and photogrammetry. Currently, the navigation community is discussing how to guarantee integrity for car navigation and multi-sensor systems.In this paper, we will give a short review on integrity concepts and on the current discussion of how to apply it to car navigation. We will discuss which role photogrammetry could play to solve the open issues in the integrity definition and monitoring for multi-sensor systems.

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Section: Current Situationmentioning

confidence: 99%

Integrity – A Topic for Photogrammetry ?

Schön

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In a typical autonomous car, available sensors are optical speed sensors, wheel encoders, Inertial Measurement Unit (IMU) (propioceptive, working at frequencies of hundreds of Hz), LiDARs and cameras (exteroceptive, working at frequencies of tens of Hz). In order to achieve the required steering and traction control accuracy, it is necessary that the state estimation module outputs a high frequency signal (hundreds of Hz) with sufficient accuracy [ 28 , 29 ]. This multi-rate environment calls for specific solutions for state estimation that we address in this paper.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Based GNSS Denied Localization for Autonomous Racing Cars

Massa

Bonamini

Settimi

et al. 2020

Sensors

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Self driving vehicles promise to bring one of the greatest technological and social revolutions of the next decade for itstheir potential to drastically change human mobility and goods transportation, in particular regarding efficiency and safety. Autonomous racing provides very similar technological issues while allowing for more extreme conditions in a safe human environment. While the software stack driving the racing car consists of several modulessuch as sensor data processing, perception, mapping, localization, planning and control, in this paper we focus on the localization problem, which provides as output the estimated pose of the vehicle needed by the planning and control modules. When driving near the friction limits, localization accuracy is critical as small errors can induce large errors in control due to the nonlinearities of the vehicle’s dynamic model. Moreover, fast and accurate pose estimation is a necessary tool to tackle corners and negotiate overtakings along the path that minimizes lap time. In this paper, we present a localization architecture for a racing car that does not rely on Global Navigation Satellite Systems (GNSS). It consists consistingof two multi-rate Extended Kalman Filters and an extension of the classicala state-of-the-art laser-based Monte Carlo localization approach that exploits some a priori knowledge of the environment and context. We first compare the proposed method with a solution based on a widely employed state-of-the-art implementation, outlining its strengths and limitations within our experimental scenario. The architecture is then tested both in simulation and experimentally on a full-scale autonomous electric racing car during an event of Roborace Season Alpha. The results show its robustness in avoiding the robot kidnapping problem typical of particle filters localization methods, while providing a smooth and high rate pose estimate. The pose error distribution depends on the car velocity, and spans on average from 0.1 m (at 60 km/h) to 1.48 m (at 200 km/h) laterally and from 1.9 m (at 100 km/h) to 4.92 m (at 200 km/h) longitudinally.

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“…Autonomous vehicles in urban environments require accurate and timely access to information such as car position, speed and so on [ 1 , 2 ]. In the future, the vehicle-to-vehicle, vehicle-to-infrastructure integrated vehicle networking systems require vehicles to share and interact with navigation information at low latency to complete the overall scheduling and improve traffic efficiency and safety [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Implementation and Performance of a Deeply-Coupled GNSS Receiver with Low-Cost MEMS Inertial Sensors for Vehicle Urban Navigation

Feng

Zhang

Lin³

et al. 2020

Sensors

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In urban environments, Global Navigation Satellite Systems (GNSS) signals are frequently attenuated, blocked or reflected, which degrades the positioning accuracy of GNSS receivers significantly. To improve the performance of GNSS receiver for vehicle urban navigation, a GNSS/INS deeply-coupled software defined receiver (GIDCSR) with a low cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) ICM-20602 is presented, in which both GPS and BDS constellations are supported. Two key technologies, that is, adaptive open-close tracking loops and INS aided pseudo-range weight control algorithm, are applied in the GIDCSR to enhance the signal tracking continuity and positioning accuracy in urban areas. To assess the performance of the proposed deep couple solution, vehicle field tests were carried out in GNSS-challenged urban environments. With the adaptive open-close tracking loops, the deep couple output carrier phase in the open sky, and improved pseudo-range accuracy before and after GNSS signal blocked. Applying the INS aided pseudo-range weight control, the pseudo-range gross errors of the deep couple decreased caused by multipath. A popular GNSS/INS tightly-coupled vehicle navigation kit from u-blox company, M8U, was tested side by side as benchmark. The test results indicate that in the GNSS-challenged urban areas, the pseudo-range quality of GIDCSR is at least 25% better than that of M8U, and GIDCSR’s horizontal positioning results are at least 69% more accurate than M8U’s.

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Localization Requirements for Autonomous Vehicles

Cited by 156 publications

References 19 publications

Integrity – A Topic for Photogrammetry ?

Integrity – A Topic for Photogrammetry ?

LiDAR-Based GNSS Denied Localization for Autonomous Racing Cars

Implementation and Performance of a Deeply-Coupled GNSS Receiver with Low-Cost MEMS Inertial Sensors for Vehicle Urban Navigation

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