# Path planning for autonomous vehicle based on heuristic searching using online images

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## Abstract

In automatic navigation of mobile systems, a path network is required to enable robot/vehicle autonomous motions. Path planning is considered as a significantly important part in creating the path network and thus to be a necessary task for any autonomous vehicle system. This paper proposes a method that constructs the shortest path for vehicle auto-navigation in outdoor environments. The method using two layers of GIS information of online map images, which support to estimate not only the shape of road network but also the directed road. This is also the advantage as compared to methods, which use only aerial/satellite images. Accomplishing the estimation according to the use of this application requires several stages as follows. First, a raw road network is detected using the road map and the satellite image. Second, the road network is refined and represented by a direct graph. Third, the road network is converted into the global coordinate, which is much more convenient for performing online auto-navigation task than the other types of coordinate. Finally, the shortest path for motion is estimated by heuristic searching method based on a hybrid algorithm that is originated from Dijkstra algorithm in a combination with greedy breadth-first search algorithm. The experimental results demonstrate robustness and effectiveness of the proposed method for path network estimation under large scenes of outdoor environments.

## Keywords

Road network detection Autonomous navigation Path planning The shortest path estimation UTM/WG84 coordinate system## 1 Introduction

Nowadays, there have been many research areas on intelligent systems, autonomous robot in outdoor environments, especially intelligent transportation in outdoor environments, such as in [1, 2, 3]. Autonomous vehicle navigation becomes an important in various applications of motion path planning, localization task. In automatic navigation of mobile systems, path planning is required to create path network for any robot/vehicle auto-traveling and considered to be the initial step in any autonomous vehicle systems. So far, researches on path planning have been achieved several milestone successes in industrial applications as well as in academic disciplines, including applications in mobile robot/vehicle and aerospace. There are many studies on the planning algorithms and implementations [4]. So far, there have been several proposed methods for road detecting and path planning [5, 6, 7]. Studies on planning algorithm and implementations have considered the issues of road detection and path planning. They can be categorized into two folds of global and local path planning methods, respectively. The global path planning is concerned with the high-level path, the whole path for movement from the source to the destination of travel itinerary. It deals with the navigation around the global region. Contrarily, the local path planning is related to the low-level path, which is suitable for further detail of specific paths. In essence, it is a segment of a certain global path but with more details to allow for avoiding local obstacle. An autonomous robot/vehicle has to deal with in reality such as determining appropriate turning-angle and speed.

The objective of this paper is to develop an efficient application for constructing the shortest path, which provides a real trajectory for autonomous vehicle navigation in the outdoor environments. Although path planning product can be provided by several commercial services, these services are typically with high cost and sometimes not all of its characteristic will be used or valid for the actual demand of a user, e.g., traffic conditions might not be useful for some applications or some special regional is not updated in the commercial version. In our proposal, the global path for vehicle motion is self-constructed using road map and satellite images, which are retrieved from free charge online service. In the case that roads are outdated in some regions of the map services, updating map is required and can be performed by road detection using aerial/satellite images. The proposed method consists of several parts as follows. A road network is estimated using road map and satellite images, which is retrieved from online map services, such as Google Maps, OpenStreetMap, and Bing Maps service. The road network is refined using some image processing techniques. The road network in image pixel coordinate is converted into the global coordinate system, so that it provides more convenient for online vehicle navigation. Finally, the shortest path for vehicle motion is estimated based on the shortest path planning algorithms, such as Dijkstra, greedy breadth-first search algorithm.

## 2 Related work and proposed method

In recent years, some of the most convincing experimental results have been obtained using promising methods for motion planning. The global path planning method based on the modification of rapidly exploring random tree algorithm is presented in [8]. The method was constructed for providing effective partial motion and achieving the global objective. Another group of researchers in [9] presented a motion planning method based on guided cluster sampling. That paper developed a point-based partially-observable Markov decision process (POMDP) approach along with a consideration of the motion error, the sensing error, and an imperfect environment map for robot’s active sensing capabilities. Experimental results show that the approach contributed an efficient method for balancing sensing and acting to accomplish given tasks in various uncertain conditions. However, the method requires high computational cost to find an optimal solution [10]. To adapt to variety and uncertain conditions, Toit et al. [11] presented a method for motion planning based on integral individual components of dynamic and uncertain environments in planning, prediction and estimation. In outdoor scenes of transposition, the traffic laws are used to estimate behaviors of the dynamic interactive systems, predict their future trajectory, and constrain the future location of the moving objects in uncertain environments. In the case of the global path planning for motion under certain maps, the computational time of that method becomes expensive when it is applied to high-level of the motion planning. Another group of authors in [12] focused on an interpolation method for optimal cost-path-motion function based on the well-known algorithms Dijkstra and A*. These authors exploited advantages of each method to provide an effective method for estimating feedback of a plan. It estimates the shortest path for motion on simplicial complex of an arbitrary dimension. The computational cost is significantly reduced by implementing an A*-like heuristic.

In the field of path planning for motion in outdoor environments, there have been some groups of researchers focusing on road detection and plan a path-trajectory for robot/vehicle motion by using aerial images [5, 6, 13, 14, 15]. Typically, the authors in [5] used a neural network to detect roads on high-resolution aerial images. In that paper, authors analyzed to learn roads based on the road surface context so that it could reduce misdetection, e.g., the roof of buildings is likelihood with the road surface without the context of surrounding scenes. Chai et al. [6] presented a method to estimate a road network based on the Monte Carlo mechanism using sampling junction-points input images. That method focused on investigating the shape and extracting the structure of a road from its nature texture. However, those methods could not overcome the case when roads are fully obscured by high buildings, tunnels and trees.

## 3 Road network detection

Different from the previous methods [5, 6], the images are retrieved from the map service with low-resolution image in this paper. The road candidates are disconnected as result of noise and other annotations of the map, as depicted in Fig. 5a, b. Notice that some places of the world maps with respect to commercial services cannot remove annotations due to the map service. To deal with this problem, a rolling ball method is proposed for connection the discontinuous roads.

The road regions are filtered based on dominant boundaries, acceptable ratio of road width/length, combing with color filter results. The results are integrated with the result of the color segmentation to discard the false detections, e.g., rivers, roof of buildings. To refine road network result, the geometry of road structure in [6] is used to post-process for improving the accuracy of road detection. The final result of network detection is show in Fig. 6c.

## 4 The global path network

## 5 The shortest path estimation

This section presents a method to estimate a path for vehicle motion with the minimal cost of feasible trajectory based on the road network configuration. There are many methods for estimating the optimal path for motion [4], e.g. Dijkstra, best-first graph search algorithm, rapidly-exploring randomized tree (RRT). The shortest path problem in this paper is considered in two-dimensional Euclidean spaces. We construct a discrete directed graph as \(G(V\),\(E)\). The set of vertex \(V=\{v_{i}\vert i=i,\ldots ,n\}\) is defined as the set of intersection and ending points of a road. The set of edges \(E=\{e_{i}\vert i=1,\ldots ,m\}\) is defined as the set of road segments between a pair of adjacent intersections or ending points. A road segment, which connects an intersection to another adjacent one or ending, is represented by two edges in the opposite direction. In the case of the one-way road, it is represented by one directed edge. The Euclidean distance is used to compute the cost of each edge based on distance of sequent points in each road segment. Given a source position \(s\) and destination position \(d\), the path planning problem is to estimate a feasible trajectory \(T\) with the lowest cost for vehicles to travel. The cost-function of trajectory is a non-negative cost, which is defined by \(c\): \(V \rightarrow R_{\ge 0}\).

The major functions of the algorithm are:

\(\mathtt{Heuristic }(u,v)\): The heuristic function estimates the distance between two nodes \(u\) and \(v\). This cost is added to make a priority in the forward direction to the destination location. In the simple case, the Euclidean distance is used to compute this cost for travel.

\(\mathtt{Push}(V, v)\): Putting a node \(v\) into the set of nodes \(V\).

\(\mathtt{Pop{\_}Lowest}(V)\): Withdrawn a node \(v\) with minimal cost to source node in the set of \(V\).

\(\mathtt{Neighbor{\_}Free}\)(*v, V,TRA*): Given a set of nodes in the set \(V\), which are directly connected with \(v\) and it was not traveled (v \(\notin \)TRA).

\(\mathtt{Path}\)(*Parent*(\(\mathtt{\upsilon }\)),*d*): Given a set of consecutive nodes of the shortest path in the set *Parent* from the current note \(\upsilon \) to the source node \(s\).

## 6 Experiment

Compare of the methods use road image and aerial image

Road image | Aerial image | |
---|---|---|

Require high-resolution images | No | Yes |

Overcome occlusion | Confident | Not confident |

Depend on update of aerial images | No | Yes |

Depend on prior knowledge of road annotation | Yes | No |

Computational time (s) | \({<}10\) | \({>}100\) |

Accuracy (%) | \({>}98.5\) | \({<}67.3\) |

The evaluation results are showed in Table 2. The sensitivity and precision criteria are used for evaluation the method. The sensitivity [True positive rate (TPR)] is computed by \({\#}\hbox {TPR} = {\#}\hbox {True positive} {/} ({\#} \hbox { True positive} + {\#} \hbox { False negative})\). The precision is computed by # \(\hbox {Precision} \!=\! {\#}\hbox {true positive {/}} ({\#} \hbox { true positive} + {\#} \hbox { false positive})\). The true positive rate and precision are affected by the zoom level, that mean under the condition of the same size of images, the result at higher zoom level is better that of lower. The road detection is perfect at the 17th zoom level and higher.

Evaluation road detection

Region | Zoom level | Segment path | Intersections/Ending | Time (ms) | ||
---|---|---|---|---|---|---|

TPR (%) | Precision (%) | TPR (%) | Precision (%) | |||

Downtown | 15 | 98.9 | 98.9 | 99.7 | 98.4 | 505 |

16 | 99.3 | 98.1 | 98.6 | 97.8 | 425 | |

17 | 100 | 99.2 | 100 | 98.8 | 381 | |

18 | 100 | 100 | 100 | 100 | 365 | |

University campus | 15 | 99.4 | 99.1 | 99.4 | 98.8 | 509 |

16 | 100 | 100 | 100 | 100 | 392 | |

17 | 100 | 100 | 100 | 100 | 347 |

## 7 Conclusion

This paper presents the method to construct an efficient shortest path planning based on the road map and satellite images for autonomous vehicle motion in outdoor environments. The results consist of path details in global coordinate, which support for control tracking for autonomous navigation. The method focuses on the estimation of the path in the global coordinates without using expensive commercial services. It consists of several stages. The road network is estimated using road map images, which is retrieved from free of charge online Google Maps services. The road network is refined and combined with the result of road detection using satellite image.For convenience in real-time online vehicle navigator, the road network in image pixel coordinate is converted into the global coordinate system. The hybrid method based on the Dijkstra algorithm in combination of greedy breadth-first search technique is applied to estimate the shortest path. By the use of road map type, which takes advantage of knowledge maps to provide high confident of the shortest path for vehicle navigation. One disadvantage of this method is that the method depends on the updating road information. This problem is compensated using satellite image. The experimental results demonstrate the effectiveness of this method from large scene of outdoor environments.

## Notes

### Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MOE) (NRF2013R1A1A2009984).

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