Publicação
3D object detection for self-driving vehicles aided by object velocity
| Resumo: | The number of road accidents still remains tragically high, being estimated that human error is the main reason behind 90% of the accidents. As such, there is a rising interest in self-driving vehicles, as these shall eliminate human error and ensure safe driving. A self-driving vehicle first needs to perceive its surroundings to drive safely. This is achievable by using vision sensors. Among these sensors, LiDAR presents the unique advantage of acquiring a high-resolution 3D representation of the surroundings. Such rich 3D data, in the form of point clouds, enables accurate 3D object detection. The success obtained from the first and current LiDAR generation has motivated the development of a second-generation LiDAR, now resorting to coherent detection. Besides estimating the coordinates of each point, such a LiDAR also estimates the radial velocity. Therefore, it is expected that the use of the radial velocity enhances 3D object detection, as it helps differentiating moving objects from static background points. This work has the main objective of analysing whether LiDAR-based 3D object detection can be improved by considering the additional feature of radial velocity. As, to the best of the author’s knowledge, no LiDAR dataset including radial velocity information is yet available, the first step taken in this work was to generate a synthetic dataset. Three use cases were created to evaluate the impact of raw and processed radial velocity features, added to ideal and flawed range estimation. Considering the radial velocity, either raw or processed, did not introduce noteworthy benefits to the detection of objects with rich geometrical information. Conversely, for objects represented by few and possibly flawed points, which are typically more challenging to detect, this information was able to improve object detection. As a conclusion, this work suggests that a second-generation LiDAR may indeed contribute to enhanced road safety. |
|---|---|
| Autores principais: | Alexandrino, Leandro Miguel Ferreira |
| Assunto: | Autonomous driving 3D object detection Coherent LiDAR Point cloud Radial velocity Deep learning |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | The number of road accidents still remains tragically high, being estimated that human error is the main reason behind 90% of the accidents. As such, there is a rising interest in self-driving vehicles, as these shall eliminate human error and ensure safe driving. A self-driving vehicle first needs to perceive its surroundings to drive safely. This is achievable by using vision sensors. Among these sensors, LiDAR presents the unique advantage of acquiring a high-resolution 3D representation of the surroundings. Such rich 3D data, in the form of point clouds, enables accurate 3D object detection. The success obtained from the first and current LiDAR generation has motivated the development of a second-generation LiDAR, now resorting to coherent detection. Besides estimating the coordinates of each point, such a LiDAR also estimates the radial velocity. Therefore, it is expected that the use of the radial velocity enhances 3D object detection, as it helps differentiating moving objects from static background points. This work has the main objective of analysing whether LiDAR-based 3D object detection can be improved by considering the additional feature of radial velocity. As, to the best of the author’s knowledge, no LiDAR dataset including radial velocity information is yet available, the first step taken in this work was to generate a synthetic dataset. Three use cases were created to evaluate the impact of raw and processed radial velocity features, added to ideal and flawed range estimation. Considering the radial velocity, either raw or processed, did not introduce noteworthy benefits to the detection of objects with rich geometrical information. Conversely, for objects represented by few and possibly flawed points, which are typically more challenging to detect, this information was able to improve object detection. As a conclusion, this work suggests that a second-generation LiDAR may indeed contribute to enhanced road safety. |
|---|
Atividades financiadas
Não foram encontrados projetos financiados.