Abstract
Achieving sufficient safety measures is among the major challenges in developing automated vehicles that can operate safely in an urban environment. Data fusion between an in-vehicle camera and a LiDAR sensor can be used for detection and tracking of other road users in an automated vehicle. In addition, simulated environments together with high-level deterministic, supervised and reinforcement learning-based autonomous control could provide traffic safety benefits in the future. These AI-based technologies have been studied in the AI4DI project to enable the Mobility as a Service (MaaS) operators fleet management of automated vehicles. The development and testing of these methods are presented in this chapter with the first promising results. The Camera - LiDAR fusion algorithm provided very good results with the accuracy evaluation using the KITTI dataset.The real-time applicability of the fusion algorithm was also successfully verified.
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence for Digitising Industry |
| Publisher | River Publishers |
| Chapter | 5.1 |
| Pages | 363-373 |
| ISBN (Electronic) | 9788770226639 |
| ISBN (Print) | 9788770226646 |
| Publication status | Published - 27 Sept 2021 |
| MoE publication type | B2 Part of a book or another research book |
Keywords
- automated driving
- sensor data fusion
- 3D object detection and tracking
- reinforcement learning
- CNN
- simulation