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Under the context of the rapid rise of smart airports, the widespread deployment of autonomous vehicles requires an efficient safety operation system. In order to develop a collision warning method based on collision probability for airport unmanned driving vehicles, using ADS-B data as a foundation, considering the interaction between aircraft and vehicles at taxiway segments and intersections. The collision probability analysis was conducted for these two types of interactive environments. Through the analysis of single-vehicle warning simulation diagrams, different levels of warning thresholds were set. When the collision probability was 0.3≤p(c)≤0.5, the following vehicle entered the secondary warning state, and the vehicle braking acceleration took a value range of 0.5~1.5 m/s2. When p(c)＞0.5, the following vehicle entered the primary warning state, and the vehicle braking acceleration took the maximum value of 2 m/s2, and carrying out simulation analysis for the same taxiway and intersection according to the set warning threshold, the simulation test showed that the collision warning method based on collision probability could calculate the probability of collisions occurring during vehicle movement on the taxiway, and perform deceleration braking according to the corresponding warning threshold, effectively reducing the possibility of collision accidents. Through Monte Carlo random simulation experiments, the collision probability change diagram under different driving modes at crossroads was obtained, and the effectiveness of the warning algorithm was verified by using hierarchical warnings for simulation analysis. The simulation experiment proved that regardless of the driving mode, the warning algorithm could effectively avoid collision conflicts, further proving that the proposed method had high adaptability. A collision probability-based collision warning method was constructs for airport unmanned driving vehicles, which can effectively avoid the occurrence of airport field collision conflicts. Meanwhile, it can significantly improve the safety of unmanned driving vehicles in the airport environment.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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