A novel model-based heuristic for energy optimal motion planning for automated driving
Zlatan Ajanovic, Michael Stolz, Martin Horn

TL;DR
This paper introduces a new heuristic for energy-efficient motion planning in automated driving, combining vehicle dynamics and operational costs to improve trajectory optimization.
Contribution
The paper proposes a novel heuristic that incorporates aerodynamic drag, auxiliary power, and operational costs into dynamic programming for energy-optimal motion planning.
Findings
Enhanced heuristic improves planning efficiency
Derived optimal cruising velocity based on vehicle properties
Compared variants show improved energy savings
Abstract
Predictive motion planning is the key to achieve energy-efficient driving, which is one of the main benefits of automated driving. Researchers have been studying the planning of velocity trajectories, a simpler form of motion planning, for over a decade now and many different methods are available. Dynamic programming has shown to be the most common choice due to its numerical background and ability to include nonlinear constraints and models. Although planning of an optimal trajectory is done in a systematic way, dynamic programming does not use any knowledge about the considered problem to guide the exploration and therefore explores all possible trajectories. A* is a search algorithm which enables using knowledge about the problem to guide the exploration to the most promising solutions first. Knowledge has to be represented in a form of a heuristic function, which gives an…
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