Modelling Object Movement Around an Ego Vehicle
| dc.contributor.author | MacIsaac, Ian | |
| dc.contributor.author | Hultberg, Johan | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper | sv |
| dc.contributor.examiner | Wolff, Krister | |
| dc.contributor.supervisor | Wolff, Krister | |
| dc.contributor.supervisor | Karlsson, Tobias | |
| dc.contributor.supervisor | Sancar, Emre | |
| dc.date.accessioned | 2019-10-17T10:48:58Z | |
| dc.date.available | 2019-10-17T10:48:58Z | |
| dc.date.issued | 2019 | sv |
| dc.date.submitted | 2019 | |
| dc.description.abstract | One problem for automated vehicles is that the tra c environment surrounding a vehicle is diverse and populated by a large set of interacting agents in the form of drivers. With a model for vehicle movement in tra c, developers will be able design autonomous vehicles with better path planning functionalities. In this thesis models for vehicle movement around an ego vehicle are developed in a data-driven manner with di erent machine learning techniques. Analysis is done to nd how accuracy is related to the prediction horizon, and to determine which features are most important. It is clear that more features are not always better as removing unnecessary features provides better results. When comparing the models, baselines based on equations of motion with constant velocity or acceleration have been used. All methods provide better predictions compared to the baselines, and can make predictions for longer horizons. For longitudinal position prediction, results are promising. In latitudinal direction the results are less impressive, especially lane changes are di cult to predict, due to the low amount of lane changes in the training data. That leads to analyzing in what other situations the prediction accuracy is limited by the data set, rather than by the model itself. For example how the accuracy is correlated with the speed of the ego vehicle. It is clear that the models performs best in situations that is well represented in the training data. To make a model that handles rare situations, a lot of data with those situations is needed. | sv |
| dc.identifier.coursecode | MMSX30 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/300476 | |
| dc.language.iso | eng | sv |
| dc.relation.ispartofseries | 2019:100 | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Autonomous Vehicles | sv |
| dc.subject | Machine Learning | sv |
| dc.subject | Data-driven | sv |
| dc.subject | Feed Forward Neural Network | sv |
| dc.subject | Linear Genetic Programming | sv |
| dc.subject | Principal Components Analysis | sv |
| dc.subject | Fishers Linear Discriminant | sv |
| dc.subject | Regression | sv |
| dc.subject | Path Prediction | sv |
| dc.title | Modelling Object Movement Around an Ego Vehicle | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Applied physics (MPAPP), MSc |