A Hybrid Vision-Map Method for Urban Road Detection

By Carlos Fernández, D. Fernández-Llorca, Miguel Angel Sotelo. 2017

21 citations recorded by the source corpus.

A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Environmental Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jacob Lambert
2. Alexander Carballo
3. Ekim Yurtsever
4. Kazuya Takeda

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Alexander Carballo
3. Jacob Lambert
4. Kazuya Takeda

In Environmental Engineering

1. Kazuya Takeda
2. Alexander Carballo
3. Ekim Yurtsever
4. Jacob Lambert

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “learning-based methods which either fuse maps with vision [200] or use fully appearance-based segmentation [208] have been used.”