# A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Driving Cost Model Based on Lane-Level Maps

#### 2.1. Lane-Level Network Structure

#### 2.2. Traffic Cost Model Based on Lane-Level Maps

#### 2.2.1. The Travel Time Cost of the Path

#### 2.2.2. Time Cost Increases Generated by Transportation Facilities

## 3. BHPS Algorithm Based on HD Maps

#### 3.1. Bidirectional Hybrid Search in Search Space

#### 3.1.1. BFS Search Process

#### 3.1.2. Dijkstra Search Process

#### 3.1.3. Bidirectional Hybrid Search Process

- (i)
- Dijkstra search space and Dijkstra path from the source node s to the relaxation region ${R}_{f}$ and from the goal node g to the relaxation region ${R}_{r}$;
- (ii)
- The BFS space near the source node s and the goal node g;
- (iii)
- Local Dijkstra search results between the relaxation regions ${R}_{f}$ and ${R}_{r}$.

#### 3.2. Design and Update of Relaxation Regions

## 4. Global Path Optimization

- (i)
- The forward Dijkstra search and reverse Dijkstra search have overlapping vertices outside the BFS search scope;
- (ii)
- The forward Dijkstra search and reverse Dijkstra search have no overlapping vertices outside the BFS search scope;

#### 4.1. Existing Overlapping Nodes

#### 4.2. With No Overlapping Nodes

## 5. Simulation Test and Analysis of Results

#### 5.1. Verification of Path Search Algorithm

#### 5.2. Simulation Test Based on HD Maps

## 6. Conclusions

- 1
- This paper establishes a model of vehicle travel time costs based on lane-level maps and abundant road network information provided by HD maps;
- 2
- The proposed algorithm uses incomplete bidirectional breadth-first search and bidirectional Dijkstra search to find the shortest path in the HD map. The algorithm not only has the high efficiency of the Dijkstra algorithm and the global optimization properties of BFS, but also greatly reduces the computational cost of the path search algorithm;
- 3
- In this paper, the self-adaption relaxation space concept is proposed, which realizes the dynamic adjustment of the search space of the proposed algorithm. This algorithm can reduce the computational load according to the demand to satisfy the computational capacity of different computing requirements.

## Author Contributions

## Funding

## Conflicts of Interest

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Dijkstra Algorithm | BFS Algorithm | A* Algorithm | BHPS Algorithm | |
---|---|---|---|---|

Search scope dimension | 102 | 171 | 38 | 143 |

Number of traffic facilities | 12 | 16 | 19 | 16 |

Length of the path (m) | 672.8 | 562.6 | 878.3 | 562.6 |

Travel time cost (s) | 89.0285 | 79.5503 | 137.4714 | 79.5503 |

Search Time (s) | 0.3022 | 0.5633 | 0.1303 | 0.4351 |

Dijkstra Algorithm | BFS Algorithm | A* Algorithm | BHPS Algorithm | |
---|---|---|---|---|

Search scope dimension | 95 | 171 | 22 | 129 |

Number of traffic facilities | 9 | 9 | 9 | 9 |

Length of the path (m) | 404.5 | 355.7 | 355.7 | 355.7 |

Travel time cost (s) | 61.89 | 55.438 | 55.438 | 55.438 |

Search Time (s) | 0.2539 | 0.4761 | 0.078 | 0.3624 |

Dijkstra Algorithm | BFS Algorithm | A* Algorithm | BHPS Algorithm | |
---|---|---|---|---|

Search scope dimension | 103 | 158 | 53 | 122 |

Number of traffic facilities | 9 | 9 | 9 | 9 |

Length of the path (m) | 814.8 | 814.8 | 1314.6 | 814.8 |

Travel time cost (s) | 121.06 | 121.06 | 192.48 | 121.06 |

Search Time (s) | 0.3283 | 0.4573 | 0.1677 | 0.3561 |

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**MDPI and ACS Style**

Yang, B.; Song, X.; Gao, Z.
A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map. *World Electr. Veh. J.* **2021**, *12*, 227.
https://doi.org/10.3390/wevj12040227

**AMA Style**

Yang B, Song X, Gao Z.
A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map. *World Electric Vehicle Journal*. 2021; 12(4):227.
https://doi.org/10.3390/wevj12040227

**Chicago/Turabian Style**

Yang, Bin, Xuewei Song, and Zhenhai Gao.
2021. "A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map" *World Electric Vehicle Journal* 12, no. 4: 227.
https://doi.org/10.3390/wevj12040227