Efficient and Enhanced Radar Perception for Autonomous Driving Systems- [electronic resource]
Efficient and Enhanced Radar Perception for Autonomous Driving Systems- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101646
- ISBN
- 9798380333689
- DDC
- 621.3
- 저자명
- Gao, Xiangyu.
- 서명/저자
- Efficient and Enhanced Radar Perception for Autonomous Driving Systems - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(136 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Roy, Sumit.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Millimeter-wave radars are increasingly integrated into commercial vehicles to support advanced driver-assistance systems, enabling robust object detection, localization, and recognition as a crucial component of environmental perception in autonomous driving systems. This thesis focuses on radar perception algorithm design, incorporating fundamental signal processing, and novel deep learning applications to address open challenges observed in autonomous driving.To tackle challenging conditions for autonomous driving, where optical sensing may be limited, we propose a novel radar multiple perspectives convolutional neural network (RAMP-CNN). This model extracts object location and class information from range-velocity-angle heatmap sequences. To reduce complexity, we combine lower-dimension network models within our RAMP-CNN, achieving significant performance enhancement. Experimental results demonstrate superior average recall and average precision compared to prior works in all testing scenarios. Notably, the RAMP-CNN model exhibits robust performance during nighttime, showcasing potential for low-cost radars as substitutes for optical sensing under adverse conditions.Current vehicular radar imaging suffers from poor azimuth resolution for side-looking operation due to antenna size constraints. To address this limitation, we propose a multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique. By applying coherent SAR principles to vehicular MIMO radar, we enhance the side-view angular resolution. The proposed MIMO-SAR algorithm employs a 2-stage hierarchical workflow, significantly reducing computation load while preserving image resolution. Coherent processing over the synthetic aperture is enabled by integrating a radar odometry algorithm to estimate the trajectory of the ego radar. Validation of the MIMO-SAR algorithm is conducted through simulations and real experiment data collected from a vehicle-mounted radar platform.Anti-collision assistance (as part of the current push towards increasing vehicular autonomy) critically depends on accurate detection/localization of moving targets in vicinity. An effective solution pathway involves removing background or static objects from the scene, so as to enhance the detection/localization of moving targets as a key component for improving overall system performance. We present an efficient algorithm for background removal for automotive scenarios, applicable to commodity frequency-modulated continuous wave (FMCW)-based radars. Our proposed algorithm follows a three-step approach: a) preprocessing of back-scattered received radar signal for 4-dimensional (4D) point clouds generation, b) 3-dimensional (3D) radar ego-motion estimation, and c) notch filter-based background removal in the azimuth-elevation-Doppler domain. The performance of our algorithm is evaluated using both simulated data and experiments with real-world data. By offering a fast and computationally efficient solution, our approach contributes to a potential pathway for challenges posed by non-homogeneous environments and real-time processing requirements.Overall, this thesis contributes to the advancement of autonomous driving systems by introducing efficient and enhanced radar perception techniques. The proposed algorithms address critical challenges, paving the way for safer and more reliable autonomous vehicles in diverse and complex driving environments.
- 일반주제명
- Electrical engineering.
- 일반주제명
- Civil engineering.
- 일반주제명
- Computer engineering.
- 키워드
- Machine learning
- 키워드
- Radar perception
- 기타저자
- University of Washington Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.