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Master Thesis proposal: Speed up Motion Prediction

Master Thesis proposal:

Speed up Motion Prediction by simultaneous and parallel execution of Motion Prediction instances.

Proper motion predictions are necessary to perform to assure safe transports with commercial heavy vehicle combinations with high productivity. The vehicle motion mgmt (VMM) needs to do motion estimation, motion prediction and motion coordination of the Motion Support Devices (MSD), see Fig 1. In motion prediction, the future motion of 1 s horizon is predicted in with current automated or manual driver inputs. These inputs can either be predicted to change or kept constant during the predictions. Multiple parallel predictions are foreseen conducted to avoid to reach the control envelope. This to avoid severe situations such as roll over, see Fig 2, understeer, oversteer, jack-knife, or trailer swingout. If the motion prediction is foreseeing a roll-over or understeer situation in the next prediction horizon. The vehicle longitudinal velocity (vx,max) capability and acceleration capability (ax,max) to the Traffic Situation Mgmt (TSM) is limited before the event occurs.

Fig. 1. Vehicle Motion Mgmt, schematic figure of system layout.


Fig. 2. Roll over lateral force limit usage on a tractor semitrailer combination.


The purpose of this master thesis contains three parts:

1. Set up parallel motion predictions in python and c++.

2. Investigate target specific parallelization strategies.

3. Deploy parallelized motion prediction models in the target environment.

3. Test in physical tractor semitrailer, especially avoid understeer, oversteer, jack-knife, and swing out in winter conditions (winter testing also conducted in simulations).

The thesis work will include control theory, vehicle dynamics, optimization and will also include SW design to take advantage of computer architecture of the specified target. The work will be carried out at Volvo Group Trucks Technology. The thesis is recommended for one or two students with control analysis, Embedded SW development profile with good mathematical skills. Thesis start: Jan 2022.

If you find this proposal interesting send your application with CV and grades to:

leo.laine@volvo.com

Contact persons:
Leo Laine – Volvo GTT
tel: +46 31 323 5311
mail: leo.laine@volvo.com

Suresh Pandian
tel: +46 739027057
mail: suresh.pandian@volvo.com

Maliheh Kani,
tel: +46 31 772 38 93
Mail: maliheh.sadeghi.kati@chalmers.se

The Volvo Group drives prosperity through transport solutions, offering trucks, buses, construction equipment, power solutions for marine and industrial applications, financing and services that increase our customers’ uptime and productivity. Founded in 1927, the Volvo Group is committed to shaping the future landscape of sustainable transport and infrastructure solutions. Countless career opportunities are offered across the group’s leading brands and entities that share a culture of Trust, Passion, High Performance, Change and Customer Success. 
www.volvogroup.com/career. 

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