Motion Prediction for vehicle combination to avoid Jack-knifing

Ready to shape tomorrow’s society together with us?


Within the technology stream Vehicle Motion and Energy Management, we develop system solutions and functionalities for Vehicle Dynamics, Vehicle Motion management and Driver assistance. These solutions must be safe, efficient and robust.
Are you ready to join our team as a thesis worker and be part of shaping the future of the transportation industry with us?


This is how you could make an impact


Proper motion predictions are necessary to perform to assure safe transports with commercial heavy vehicle combinations with high productivity. In motion prediction, the future motion of 1s horizon is predicted with current automated or manual driver inputs. Multiple parallel predictions are to be conducted to avoid yaw instability situations such as jack-knife, or trailer swingout and proper coordination of the available actuators should be performed.
The purpose of this master thesis is to propose a new system for detecting and predicting articulated vehicles’ jackknifing or trailer swing. This system uses on one hand, a vehicle model of articulated vehicle making it possible to determine the vehicle dynamics. On the other hand, detection algorithm is based on a jackknifing criterion and on the prediction function of jackknifing, in view of estimating the time to jackknifing.
The motion detection and prediction should be derived and implemented in Python and C++. The proposed methods should be validated in close loop with high fidelity vehicle plant model. In the final stage, real tests should be performed in physical tractor + semitrailer, especially avoiding yaw instabilities in winter conditions (if there is possibility for real test and winter testing, otherwise it can be also conducted in simulations using high fidelity vehicle model).
The Master thesis work will include control theory, vehicle dynamics and optimization. The work will be carried out at Volvo Group Trucks Technology. The thesis is recommended for one or two students with control analysis profile with good mathematical skills.
If you find this proposal interesting send your application with CV and grades.


Supervisor:
Maliheh Sadeghi Kati, Research Engineer, +46 739026567


Kindly note that due to GDPR, we will not accept applications via mail. Please use our career site.