@inproceedings {pub3302,
	title = {Improving spatial trajectory planning by using an enhanced road representation},
	author = {Edoardo Casapietra AND Thomas H Weisswange AND Franz Kummert AND Christian Goerick},
	year = {2017},
	month = {September},
	abstract = {The detection of road layout and semantics is an important issue in modern ADAS and autonomous driving systems. In particular, trajectory planning needs a spatial road representation to operate on. As typical trajectories are computed for time-spans in the order of a few seconds, the spatial range needed for the road representation to achieve a stable and smooth trajectory can go from tenths to hundreds of meters. This range is very hard to achieve by using direct road detection, especially in inner-city, due to occlusions and hardware limitations. State-of-the-art systems cope with this problem by employing annotated road maps to complement direct detection. However, maps are expensive to make and not available on every road. Furthermore, ego-localization is a key issue in their usage. 
In this paper we present a system that employs grid-based spatial road representation derived from both direct and indirect road detection. Direct detection on monocular images is provided by RTDS[Fritsch2014], while indirect detection is based upon the interpretation of the other vehicles{\textquoteright} behavior. The addition of indirect detection, exploiting the scenarios that are more challenging for direct detection, improves the spatial range of the representation and allows the inclusion of additional semantics, such as lanes and driving directions. 
The road representation is evaluated by comparison with a ground truth obtained from real world data. In order to also demonstrate its advantages for trajectory planning, we couple it with a basic trajectory planner, which receives the current position of the ego-car and a target area (from a navigational map), and computes the optimal trajectory. We compare different trajectories obtained using our full representation with ones obtained using only RTDS or navigational maps to show advantages in terms of lower jerk and higher stability over time of the optimal path.},
	publisher = {SAE},
	booktitle = {4th International Symposium on Future Active Safety Technology: Toward zero traffic accidents (FAST-zero{\textquoteright}17)},
	city = {Nara, Japan}
}