In autonomous driving, sensing is the most fundamental task providing the necessary information for intelligent vehicles. Compared with single-vehicle sensing, cooperative sensing can greatly reduce the cost, increase the accuracy and overcome the vision range limit. In this paper, a cooperative sensing framework is proposed based upon the occupancy grid map. For map formation, we investigate four occupancy probability distribution algorithms to transform the sensor data into a probability model; for map fusion, we design a probability fusion method to combine multi-vehicle maps. Simulations show that the proposed probability distribution algorithms can capture the environment information with different focuses and the map fusion process can expand the vehicles' sensing range and improve the accuracy.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Communication Systems, ICCS 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jul 2 2018|
|Event||16th IEEE International Conference on Communication Systems, ICCS 2018 - Chengdu, China|
Duration: Dec 19 2018 → Dec 21 2018
|Name||2018 IEEE International Conference on Communication Systems, ICCS 2018|
|Conference||16th IEEE International Conference on Communication Systems, ICCS 2018|
|Period||12/19/18 → 12/21/18|
Bibliographical noteFunding Information:
This work was in part supported by the National Natural Science Foundation of China under Grants 61622101 and 61571020, the National Science and Technology Major Project under Grant 2018ZX03001031, and the Major Project from Beijing Municipal Science and Technology Commission under Grant Z181100003218007.
© 2018 IEEE.
- Autonomous vehicle
- cooperative sensing
- map fusion
- occupancy probability distribution