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261	Däckmodellering och prestandaanalys för Formula Student Olsson, Hugo January 2024 (has links) Arbetets syfte var att göra ett motiverat val av däck till bilen som KTH Formula Student planerar att utveckla till 2025. Detta gjordes genom att använda däckdata från FSAE TTC och utifrån den modellera alla däck med däckmodellen Magic Formula TNO MFTyre/MF-Swift 6.1. Denna modell innehåller variationer i däcktryck, camber och vertikal last. Därefter simulerades varvtid vid olika grenar som skidpad, autocross och endurance för att jämföra alla däcks prestanda. Dessutom kunde flera andra värden värda att jämföras beräknas utifrån däckmodellen och datan från FSAE TTC. Dessa var hållbarhet, däcktemperatur, däckens massa, sidkraftskoefficient, camberkänslighet och däckens fjäderkonstant. Alla dessa värden jämfördes till sist där varvtid och hållbarhet prioriterades. Tre däck fanns kvar i slutet vilka rekommenderades. Formula Student Formula SAE KTH Formula Student Magic Formula Hjul Däck Modellering Tyre Tire Modelling Modeling Däckmodellering Tyre modelling Tire modeling Tyre model Tire model Ackermann Styrningsgeometri Steering geometry MATLAB Simuleringsteknik Simulation Fordonsteknik Automotive Engineering Fordonsdynamik Vehicle Dynamics Väghållning Handling Tyre choice Tire choice Däckval Race Engineering Varvtidssimulator Lap Time Simulation Performance Optimization Elbil Electric Vehicle Suspension Suspension design Hjulupphängning Fjädring Engineering and Technology Teknik och teknologier
262	Návrh zavěšení kol Formule Student / Design of Formula Student Wheel Suspensions Urban, Marek January 2020 (has links) Tato práce se se zabývá návrhem kinematiky zavěšení kol obou náprav. Na základě analýz jízdních dat, multi-body simulací v softwaru Adams Car, simulací v Matlabu a analytických kalkulací v Mathcadu, je navržena řada změn s cílem zlepšit jízdní vlastnosti vozu Formule student, tyto změny jsou následně implementovány do CAD modelu vozu. Jednotlivé změny kinematiky náprav jsou provedeny na základě analýzy konkrétního problému, který se snaží řešit. Jednou z problematik je zástavbová náročnost systému odpružení a zavěšení zadních kol, zde je cílem snížit hmotnost, výšku těžiště a moment setrvačnosti. Další problematikou je geometrie předního kola, kde je cílem zlepšit využití pneumatik a snížit síly v řízení. Dále se práce zabývá simulacemi elastokinematiky zadní nápravy, součástí je také návrh měřícího zařízení. V poslední části je zkoumán vliv provedených změn i elastokinematiky na jízdní dynamiku vozu v ustálených stavech za pomocí MM metody simulované s modelem celého vozu v Adams Car a zpracované v Matlabu.
263	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) 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. 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 Control Engineering Reglerteknik

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