| 1. |
- Ardeshiri, Tohid, 1980, et al.
(författare)
-
Offset Eliminative Map Matching Algorithm for Intersection Active Safety
- 2006
-
Ingår i: 2006 IEEE Intelligent Vehicles Symposium, IV 2006; Meguro-Ku, Tokyo; Japan; 13 June 2006 through 15 June 2006. - 9784901122863 ; :1689609, s. 82-88
-
Konferensbidrag (refereegranskat)abstract
- Digital map information and Continuous Positioning Systems (CPS) are being increasingly used in active safety applications. However due to imprecision associated with digital road maps and inevitable inaccuracies in CPS positions, a map matching algorithm is essentialfor these applications.One field of active safety in which navigation information can be used is Intersection Active Safety Applications (IASA) which requires a precise position of vehicle relative to road network in an intersection. In this paper a novel map matching algorithm for an IASA is presented.To determine the vehicle trajectory relative to the road network, the proposed map matching algorithm calculates the general offset between digital road map and the CPS given vehicle trajectory by fusion of local offsets with a Kalman filter, incorporating their respective ncertainties. The created offset liminative map matching algorithm was tested on a omplex urban trajectory and showed very ncouraging results.
|
|
| 2. |
- Ardeshiri, Tohid, 1980, et al.
(författare)
-
Sensor Fusion for Vehicle Positioning in Intersection Active Safety Applications
- 2006
-
Ingår i: 8th International Symposium on Advanced Vehicle Control. - 9860059470
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Global Positioning System (GPS) is being increasingly used in active safety applications. One field of active safety in which navigation information can be used is Intersection Active Safety Applications (IASA) which requires a precise and continuous estimate of vehicle position and heading direction to function properly. In this paper an implementation of Extended Kalman Filter for estimation of vehicle position and heading direction in an intersection active safety application is presented. The algorithm was tested on a complex urban trajectory of 2 km long and showed very encouraging results.
|
|
| 3. |
- Ardeshiri, Tohid, 1980-, et al.
(författare)
-
An adaptive PHD filter for tracking with unknown sensor characteristics
- 2013
-
Konferensbidrag (refereegranskat)abstract
- In multi-target tracking, the discrepancy between the nominal and the true values of the model parameters might result in poor performance. In this paper, an adaptive Probability Hypothesis Density (PHD) filter is proposed which accounts for sensor parameter uncertainty. Variational Bayes technique is used for approximate inference which provides analytic expressions for the PHD recursions analogous to the Gaussian mixture implementation of the PHD filter. The proposed method is evaluated in a multi-target tracking scenario. The improvement in the performance is shown in simulations.
|
|
| 4. |
- Ardeshiri, Tohid, 1980-, et al.
(författare)
-
Convex Optimization Approach for Time-Optimal Path Tracking of Robots with Speed Dependent Constraints
- 2010
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The task of generating time optimal trajectories for a six degrees of freedom industrial robot is discussed and an existing convex optimization formulation of the problem is extended to include new types of constraints. The new constraints are speed dependent and can be motivated from physical modeling of the motors and the drive system. It is shown how the speed dependent constraints should be added in order to keep the convexity of the overall problem. A method to, conservatively, approximate the linear speed dependent constraints by a convex constraint is also proposed. A numerical example proves versatility of the extension proposed in this paper.
|
|
| 5. |
- Ardeshiri, Tohid, 1980-, et al.
(författare)
-
Convex Optimization Approach for Time-Optimal Path Tracking of Robots with Speed Dependent Constraints
- 2011
-
Ingår i: Proceedings of the 18th IFAC World Congress. - : IFAC. - 9783902661937 ; , s. 14648-14653
-
Konferensbidrag (refereegranskat)abstract
- The task of generating time optimal trajectories for a six degrees of freedom industrial robot is discussed and an existing convex optimization formulation of the problem is extended to include new types of constraints. The new constraints are speed dependent and can be motivated from physical modeling of the motors and the drive system. It is shown how the speed dependent constraints should be added in order to keep the convexity of the overall problem. A method to, conservatively, approximate the linear speed dependent constraints by a convex constraint is also proposed. A numerical example proves versatility of the extension proposed in this paper.
|
|
| 6. |
|
|
| 7. |
- Ardeshiri, Tohid, 1980-, et al.
(författare)
-
On Reduction of Mixtures of the Exponential Family Distributions
- 2013
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Many estimation problems require a mixture reduction algorithm with which an increasing number of mixture components are reduced to a tractable level. In this technical report a discussion on dierent aspects of mixture reduction is given followed by a presentation of numerical simulation on reduction of mixture densities where the component density belongs to the exponential family of distributions.
|
|
| 8. |
- Svanberg, Jan, et al.
(författare)
-
Corporate governance performance ratings with machine learning
- 2022
-
Ingår i: International Journal of Intelligent Systems in Accounting, Finance & Management. - : John Wiley & Sons. - 1055-615X .- 1099-1174 .- 1550-1949. ; 29:1, s. 50-68
-
Tidskriftsartikel (refereegranskat)abstract
- We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.
|
|
| 9. |
- Svanberg, Jan, et al.
(författare)
-
Prediction of environmental controversies and development of a corporate environmental performance rating methodology
- 2022
-
Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526 .- 1879-1786. ; 344
-
Tidskriftsartikel (refereegranskat)abstract
- Institutional investors seek to make environmentally sustainable investments using environment, social, governance (ESG) ratings. Current ESG ratings have limited validity because they are based on idiosyncratic scores derived using subjective, discretionary methodologies. We discuss a new direction for developing corporate environmental performance (CEP) ratings and propose a solution to the limited validity problem by anchoring such ratings in environmental controversies. The study uses a novel machine learning approach to make the ratings more comprehensive and transparent, based on a set of algorithmic approaches that handle nonlinearity when aggregating ESG indicators. This approach minimizes the rater subjectivity and preferences inherent in traditional ESG indicators. The findings indicate that controversies as proxies for non-compliance with environmental responsibilities can be predicted well. We conclude that environmental performance ratings developed using our machine learning framework offer predictive validity consistent with institutional investors' demand for socially responsible investment screening.
|
|
