| 1. |
- Hansson, Anders, et al.
(författare)
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Lane-Level Map Matching based on HMM
- 2021
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 6:3, s. 430-439
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Tidskriftsartikel (refereegranskat)abstract
- Lane-level map matching is essential for autonomous driving. In this paper, we propose a Hidden Markov Model (HMM) for matching a trajectory of noisy GPS measurements to the road lanes in which the vehicle records its positions. To our knowledge, this is the first time that HMM is used for lanelevel map matching. Apart from GPS values, the model is further assisted by yaw rate data (converted to a lane change indicator signal) and visual cues in the form of the left and right lane marking types (dashed, solid, etc.). Having defined expressions for the HMM emission and transition probabilities, we evaluate our model to demonstrate that it achieves 95.1% recall and 3.3% median path length error for motorway trajectories.
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| 2. |
- Maghsood, Roza, 1980, et al.
(författare)
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Detection of steering events based on vehicle logging data using hidden Markov models
- 2016
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Ingår i: International Journal of Vehicle Design. - : Inderscience Publishers. - 0143-3369 .- 1741-5314. ; 70:3, s. 278-295
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Tidskriftsartikel (refereegranskat)abstract
- In vehicle design it is desirable to model the loads by describing load environment, customer usage and vehicle dynamics. In this study a method will be proposed for detection of steering events such as curves and manoeuvring using on-board logging signals available on trucks. The method is based on hidden Markov models (HMMs), which are probabilistic models that can be used to recognise patterns in time series data. In an HMM, 'hidden' refers to a Markov chain where the states are not observable. However, observations depending on the hidden Markov chain can be observed. The idea here is to consider the current driving event as the hidden state, while the on-board logging signals generate the observed sequence. Examples of curve detection are presented for both simulated and measured data on a truck. The classification results indicate that the method can recognise left and right turns with small misclassification errors.
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| 3. |
- Maghsood, Roza, et al.
(författare)
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Detection of steering events based on vehicle logging data using hidden Markov models
- 2016
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Ingår i: International Journal of Vehicle Design. - : Inderscience Enterprises Ltd. - 0143-3369 .- 1741-5314. ; 70:3, s. 278-295
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Tidskriftsartikel (refereegranskat)abstract
- In vehicle design it is desirable to model the loads by describing load environment, customer usage and vehicle dynamics. In this study a method will be proposed for detection of steering events such as curves and manoeuvring using on-board logging signals available on trucks. The method is based on hidden Markov models (HMMs), which are probabilistic models that can be used to recognise patterns in time series data. In an HMM, 'hidden' refers to a Markov chain where the states are not observable. However, observations depending on the hidden Markov chain can be observed. The idea here is to consider the current driving event as the hidden state, while the on-board logging signals generate the observed sequence. Examples of curve detection are presented for both simulated and measured data on a truck. The classification results indicate that the method can recognise left and right turns with small misclassification errors.
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| 4. |
- Maghsood, Roza, 1980, et al.
(författare)
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Detection of steering events using hidden Markov models with multivariate observations
- 2016
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Ingår i: International Journal of Vehicle Systems Modelling and Testing. - 1745-6436 .- 1745-6444. ; 11:4, s. 313-329
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Tidskriftsartikel (refereegranskat)abstract
- In this article we propose a method to identify steering events, such as curves and manoeuvres for vehicles. We use a hidden Markov model with multidimensional observations, to estimate the number of events. Three signals, lateral acceleration, steering angle speed and vehicle speed, are used as observations. We demonstrate that hidden Markov models with a combination of continuous and discrete distributions for observations can be used to detect steering events. Further, the expected number of events is estimated using the transition matrix of hidden states. The results from both measured and simulated data show that the method works well and accurately estimates the number of steering events.
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| 5. |
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| 6. |
- Maghsood, Roza, 1980, et al.
(författare)
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Detection of the Curves based on Lateral Acceleration using Hidden Markov Models
- 2013
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Ingår i: Fatigue Design 2013, International Conference Proceedings. - : Elsevier BV. - 1877-7058. ; 66, s. 425-434
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Konferensbidrag (refereegranskat)abstract
- In vehicle design it is desirable to model the loads by describing the load environment, the customer usage and the vehicle dynamics. In this study a method will be proposed for detection of curves using a lateral acceleration signal. The method is based on hidden Markov models (HMMs) which are probabilistic models that can be used to recognize patterns in time series data. In an HMM, 'hidden' refers to a Markov chain where the states are not observable, however what can be observed is a sequence of data where each observation is a random variable whose distribution depends on the current hidden state. The idea here is to consider the current driving event as the hidden state and the lateral acceleration signal as the observed sequence. Examples of curve detection are presented for both simulated and measured data. The classification results indicate that the method can recognize left and right turns with small misclassification errors.
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| 7. |
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| 8. |
- Maghsood, Roza, 1980, et al.
(författare)
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Load description and damage evaluation using vehicle independent driving events
- 2015
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Ingår i: Procedia Engineering. - : Elsevier BV. - 1877-7058. ; 101, s. 268-276
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Tidskriftsartikel (refereegranskat)abstract
- We consider the loads that are related to steering events, and focus on the events that cause high forces on steering components. The load is simplified by keeping the extreme force value for each driving event. We define a simplified stochastic model for the load by modeling the extreme value for each driving event by a random variable. We give formulas to compute the theoretical load spectrum and the expected fatigue damage caused by the driving events. Further, in a sensitivity study we investigate how much the expected damage depends on the variability of parameters of the proposed model.
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| 9. |
- Maghsood, Roza, 1980, et al.
(författare)
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Modeling extreme loads acting on steering components using driving events
- 2015
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Ingår i: Probabilistic Engineering Mechanics. - : Elsevier BV. - 0266-8920 .- 1878-4275. ; 41, s. 13-20
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Tidskriftsartikel (refereegranskat)abstract
- Forces during steering events, such as curves and maneuvers, cause large stresses on steering components. In this paper, we formulate a model for the lateral loads causing fatigue damage of the steering components. Steering events are identified using a Hidden Markov model on the CAN (Controller Area Network) bus data. The CAN data is available on all vehicles, thus the model is applicable across many types of vehicles. To identify the events, the observation from CAN data is modeled by a multivariate generalized Laplace (GAL) distribution. An explicit formula for the expected fatigue damage is given. Results are validated using measured lateral acceleration.
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