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Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms
Linköping University, Department of Electrical Engineering, Automatic Control.
Linköping University, Department of Electrical Engineering, Automatic Control.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development.

An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering.

Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth.

Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.

Place, publisher, year, edition, pages
2019. , p. 83
Keywords [en]
evaluation, target tracking, multiple sensors, non-causal, smoother, smoothing, tracking, vehicle tracking, camera, lidar, estimate, estimation, prediction, vehicle dynamics, sensor fusion, real-time tracking, extended kalman filter, filter validation, validation, position estimation, velocity estimation, dynamic model, model complexity, multi object tracking, multiple object, tracking, single object tracking, data association, tracking fundamentals, iterated kalman filter, track management, gnn, global nearest neighbour, mahalanobis, mahalanobis distance, performance evaluation, differential gps, dgps, roi, ego, several sensors, sensors, rmse, root mean square error, invertible motion, anti-causal motion, anti-causal tracking, constant velocity, gnn, imu, tfs, two filter smoother, ekf, rts, radar, inertial measurement unit, nonlinear, nonlinear systems, mono camera, monocular camera, noise model, tracking performance, fixed interval smoothing, m/n logic, centralized fusion, non-causal object tracker, car tracking, car dynamics, automotive, active safety, object tracking, automotive industry, thesis, master, reverse dynamics, reverse tracking, reverse sequence, sequence tracking, data propagation, ground truth, estimating ground truth, additional sensors, mounted sensors, true estimates, environment, comparison, algorithm, independent targets, overlapping, measurements, occluded, track switch, improve, lower, uncertainty, more, certain, state, process, noise, covariance, sampling, image, sprt, adas, cnn, cv, pdf, track, target, ego, tracker, tentative track, observatiom, online tracking, offline tracking, online, offline, recorded, sequences, robust, self driving, self-driving, car, traffic, trajectory, true state, scenario, scenarios, future, accurate, output, advanced, driver, assistance, systems, non-linear, complex noise, pedestrian, truck, bus, maneuvering, vehicles, processed, measurement, frame, state, correction, probability, density, function, tuning, likelihood, transition, measurement, motion, model, recursion, gaussian, approximation, distribution, linear, jacobian, multiplicative, noise, ratio, ad, hoc, ad hoc, state, space, approach, backward, auction, euclidean, distance, statistical, threshold, gating, association, margin, normalize, covariance, matrix, fusion, confirmed, rejected, tentative, history, absolute, error, modular, ego motion, parameters, variables, logg, hardware, specification, fused, causal, factorization, independent, uncorrelated, transform, moving, rotation, translation, oncoming, overtaking
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-160020ISRN: LiTH-ISY-EX--19/5256--SEOAI: oai:DiVA.org:liu-160020DiVA, id: diva2:1348051
External cooperation
Veoneer Sweden AB
Subject / course
Automatic Control
Presentation
2019-08-30, Visionen, Linköping, 10:00 (Swedish)
Supervisors
Examiners
Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-09-03Bibliographically approved

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