2023-12-02-dauner23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Parting with Misconceptions about Learning-based Vehicle Motion Planning	Poster	o82EXEK5hu6	The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed, we find that these tasks are fundamentally misaligned and should be addressed independently. We further assess the current state of closed-loop planning in the field, revealing the limitations of learning-based methods in complex real-world scenarios and the value of simple rule-based priors such as centerline selection through lane graph search algorithms. More surprisingly, for the open-loop sub-task, we observe that the best results are achieved when using only this centerline as scene context (i.e., ignoring all information regarding the map and other agents). Combining these insights, we propose an extremely simple and efficient planner which outperforms an extensive set of competitors, winning the nuPlan planning challenge 2023.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	dauner23a	0	Parting with Misconceptions about Learning-based Vehicle Motion Planning	1268	1281	1268-1281	1268	false	Dauner, Daniel and Hallgarten, Marcel and Geiger, Andreas and Chitta, Kashyap	given family Daniel Dauner given family Marcel Hallgarten given family Andreas Geiger given family Kashyap Chitta	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/dauner23a/dauner23a.pdf