Abstract
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.
Highlights
Autonomous vehicles require a precise and robust perception of the environment
The evaluation method used to measure the quantitative results is the F1-score since this score is used in the Classifier decision trees (DT) Random Trees (RT) Extremely Randomized Trees (ERT) Boosting
The dataset consists of 289 images and is divided into three types of scenes: the first is urban marked (UM) roads, the second is Urban Multiple Marked (UMM) lanes, and the third is Urban Unmarked (UU) roads
Summary
Autonomous vehicles require a precise and robust perception of the environment. This is a crucial point in the development of autonomous vehicles because the perception layer is the base of higher level systems, such as control algorithms or path planning. ADAS have mainly focused on increasing the safety of drivers and road users by means of driver warnings and assisted interventions. It is undebatable that autonomous driving has become a high priority issue on the research and commercial agendas of major car makers over recent years, with these makers intending to produce fully autonomous vehicles by 2020. The deployment of autonomous cars will bring a number of clear benefits in terms of increased traffic efficiency and reduced accident toll, resulting in unquestionably higher energy efficiency and enhanced road safety
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