Vision and GPS-based autonomous vehicle navigation using ...

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Figure 2: General outline of the vision and GPS based autonomous navigation system. Our approach is based on two ANNs. The first identifies navigable regions using a template-based algorithm. It classifies the image and identifies the actions that should be taken by CaRINA. Images are acquired and then processed using the ANNs, which identify the road ahead of the vehicle. The value of the azimuth (difference between the current and target positions) as well as a set of values that correspond to navigable and non-navigable regions (free or obstructed) of the road is obtained. These values are used as the input of the second ANN, which aims to learn the control rules (supervised training data) for vehicle control, providing steering and velocity values. Several ANN topologies have been evaluated in order to obtain the minimum training error. The system is detailed in section 3. 2. RELATED WORKS Autonomous Land Vehicle in a Neural Network (ALVINN) [11] is an ANN-based navigation system that calculates a steer angle to keep an autonomous vehicle in the road limits. The gray-scale levels of a 30 x 32 image were used as the input of an ANN. The original road image and steering were generated to improve the training, allowing ALVINN to learn how to navigate in new roads. Low resolution of a 30 x 32 image and high computational time are some of the problems found. The architecture comprises 960 input units fully connected to a hidden layer by 4 units, also fully connected to 30 units in an output layer. As this approach requires real time decisions, this topology is not efficient. 281 Chan et al. [2] shows an Intelligent Speed Adaptation and Steering Control that allows the vehicle to anticipate and negotiate curves safely. It uses Generic Self-Organizing Fuzzy Neural Network (GenSoFNN-Yager) which include the Yager inference scheme [8]. GenSoFNN-Yager has as feature their ability to induce from low-level information in form of fuzzy if-then rules. Results show the robustness of the system in learning from example human driving, negotiating new unseen roads. The autonomous driver demonstrates that to anticipate is not always sufficient. Moreover, large variations in the distribution of the rules were observed, which imply a high complexity of the system. Stein and Santos [18] system’s computes the steering of an autonomous robot, moving in a road-like environment. It uses ANNs to learn behaviors based on examples from a human driver, replicating and sometimes even improving human-like behaviors. To validate the created ANNs, real tests were performed and the robot successfully completed several laps of the test circuit showing good capacities for both recovery and generalization with relatively small data sets. One of the issues found is the impossibility of validating network training without testing it with the real robot. Markelic et al. [5], proposes a system that learns driving skills based on a human teacher. Driving School (DRIVSCO) is implemented as a multi-threaded, parallel CPU/GPU architecture in a real car and trained with real driving data to generate steering and acceleration control for road following. Furthermore, it uses an algorithm to detect independently-
(a) (b) (c) (d) Figure 3: Classifier structure (a), real image (b), image processing step (c) and template matching (T) (d). moving objects (IMOs) to spot obstacles with a stereo camera. A predicted action sequence is compared to the driver’s actions and a warning is issued if there are many differences between the two actions. The IMO detection algorithm is more general as it will respond not only to cars, but also to any sufficiently large (11 x 11 pixels) moving object. The steering prediction is very close to the human signal, but the acceleration is less reliable. 3. PROPOSED METHOD Our approach is composed of 4 steps. In the first step an image is obtained and through ANNs another image is generated, identifying where the road (navigable region) is (Figure 3 (c)). In the second step, a template matching algorithm is used to identify the geometry of the road ahead of the vehicle (straight line, soft turn, or hard turn). In the third step, an occupation percentage is calculated to obtain the occupation areas, verifying if the road is obstructed or free based on the classified image. Finally, a supervised learning technique is used to define the action that the vehicle should take to remain in the safe navigation regions (road). These steps are described in the next subsections. 3.1 Image Processing Step The proposed method by Shinzato [13] was used here, as it applies ANNs to a road identification task. A system composed of six Multilayer Perceptron (MLP) ANNs was proposed to identify the navigable regions in urban environments (Figure 3 (a)). The real image can be seen on Figure 3 (b). The result of this ANNs output combination is a navigability map (Figure 3 (c)). The brighter blocks represent the more likely area to be considered navigable. This step divides an image into blocks of pixels and evaluates them as single units. The advantage is that it can train the ANNs to identify different types of navigable and non-navigable regions (e.g. paved and non-paved roads, sidewalks). Initially, the image is divided into blocks of pixels, which are individually evaluated. Several features, such as pixel attributes, like RGB average, image entropy and other features obtained from this collection of pixels are calculated for each block. In the grouping step, a frame with (M xN ) pixels resolution is sliced into groups with (K xK ) pixels. Suppose an image represented by a matrix I of size (M xN ). Element I(m,n) corresponds to the pixel in row m and column n of the image, where (0
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Page 5 and 6: Table 2: Results of the ANNs for ea

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