Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.

Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Evaluating Localization Accuracy of Automated Driving Systems

Rehrl¹,

Gröchenig²

2021

“…Assuming that noise can be modeled as Gaussian white noise, the EKF provides a suboptimal technique for non-linear model and estimates system state by means of a least square error approach. A description of the different data fusion techniques is presented in [ 23 , 24 , 25 , 26 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy

Alteriis

Accardo

Conte

et al. 2021

The paper deals with performance enhancement of low-cost, consumer-grade inertial sensors realized by means of Micro Electro-Mechanical Systems (MEMS) technology. Focusing their attention on the reduction of bias instability and random walk-driven drift of cost-effective MEMS accelerometers and gyroscopes, the authors hereinafter propose a suitable method, based on a redundant configuration and complemented with a proper measurement procedure, to improve the performance of low-cost, consumer-grade MEMS sensors. The performance of the method is assessed by means of an adequate prototype and compared with that assured by a commercial, expensive, tactical-grade MEMS inertial measurement unit, taken as reference. Obtained results highlight the promising reliability and efficacy of the method in estimating position, velocity, and attitude of vehicles; in particular, bias instability and random walk reduction greater than 25% is, in fact, experienced. Moreover, differences as low as 0.025 rad and 0.89 m are obtained when comparing position and attitude estimates provided by the prototype and those granted by the tactical-grade MEMS IMU.

“…The proposed model offers better positioning performance than individual sub-models for a relative navigation system with GNSS and ultra wideband observations. Reference [20] discusses a Kalman integrated PL approach with empirically tuned parameters. It demonstrates better performance of the new approach over two other methods for an autonomous vehicle state estimation with GNSS, INS, and an odometer.…”

Section: Introductionmentioning

confidence: 99%

“…Five of the aforementioned references discuss KF-based PL calculations. Among them, [10,15] perform fault detection in the position domain, [16,17] follow range domain methods, and [20] assumes that fault detection and mitigation have been executed. This paper attempts to explore range domain methods further for their relatively low architectural complexity.…”

Section: Introductionmentioning

confidence: 99%

Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

Bhattacharyya

2021

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.