Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving

Wang, Ke; Zhou, Tianqiang; Li, Xingcan; Ren, Fan

doi:10.1109/tiv.2022.3213796

Cited by 50 publications

(14 citation statements)

References 183 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Grid-based 3D object detection has gained significant interest in recent years, especially in applications like autonomous driving [4], [44], [45] and robotics. Grid-based split methods offer a structured way to handle the inherent sparsity and irregularity of point clouds.…”

Section: A Grid-based 3d Object Detectionmentioning

confidence: 99%

PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection

Mao,

Wang,

Zhang

et al. 2024

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

Pillar-based 3D object detectors mainly employ randomly initialized 2D convolution neural network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone scaling and pretraining in the image domain. This paper shows the effectiveness of 2D backbone scaling and pretraining for pillar-based 3D object detectors. For better backbone scaling, we first introduce several design principles for point cloud backbone, to tackle the sparsity of point cloud and improve the effective receptive field. The backbone scaling is achieved by adaptively designed based on the model size. For backbone pretraining, we propose a weight adaptation module, to transfer the image knowledge obtained by pretraining on large-scale image datasets for the point cloud. Our proposed pillar-based detector, termed PillarNeSt, outperforms the existing 3D object detectors by a large margin on the nuScenes and Argoversev2 datasets. Code is released at https://github.com/WayneMao/PillarNeSt.

show abstract

Section: A Grid-based 3d Object Detectionmentioning

confidence: 99%

PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection

Mao,

Wang,

Zhang

et al. 2024

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…Facing different testing purposes, researchers focus on some specific attributes. For example, to evaluate the robustness of the perception module under different weather conditions, researchers may focus more on the configurations of different weathers, such as rain and fog [9,41,51]. To evaluate the safety of an ADS, researchers focus more on the configurations of the trajectories and behaviors of the NPC vehicles [23,28,45].…”

Section: Scenario Descriptionmentioning

confidence: 99%

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Cheng

Zhou

Xie

2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

View full text Add to dashboard Cite

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performance and increase failure analysis costs.In this paper, we present a novel behavior-guided fuzzing technique (BehAVExplor) to explore the different behaviors of the ego vehicle (i.e., the vehicle controlled by the ADS under test) and detect diverse violations. Specifically, we design an efficient unsupervised model, called BehaviorMiner, to characterize the behavior of the ego vehicle. BehaviorMiner extracts the temporal features from the given scenarios and performs a clustering-based abstraction to group behaviors with similar features into abstract states. A new test case will be added to the seed corpus if it triggers new behaviors (e.g., cover new abstract states). Due to the potential conflict between the behavior diversity and the general violation feedback, we further propose an energy mechanism to guide the seed selection and the mutation. The energy of a seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an industrial-level ADS, and LGSVL simulation environment. Empirical evaluation results show that BehAVExplor can effectively find more diverse violations than the state-of-the-art. CCS CONCEPTS• Software and its engineering → Software testing and debugging.

show abstract

“…T HE past decade has witnessed the significant breakthroughs on autonomous driving with artificial intelligence methods [2], [3], leading to numerous applications in transportation, including improving traffic safety [4]- [6], reducing traffic congestion [7], [8], minimizing air pollution [9], [10], and enhancing traffic efficiency [11]- [13]. Object detection is a critical component of autonomous driving, which relies on computer vision and artificial intelligence techniques to understand driving scenarios [2], [14]. However, the foggy and rainy weather conditions make the understanding of camera images particularly difficult, which poses challenges to the camera based object detection system installed on the intelligent vehicles [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

et al. 2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, however such assumption may fail in different weather conditions. Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. Overcoming detection bottlenecks in foggy and rainy weather is a real challenge for autonomous vehicles deployed in the wild. To bridge the domain gap and improve the performance of object detectionin foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection. The adaptations at both the image-level and objectlevel are intended to minimize the differences in image style and object appearance between domains. Furthermore, in order to improve the model's performance on challenging examples, we introduce a novel adversarial gradient reversal layer that conducts adversarial mining on difficult instances in addition to domain adaptation. Additionally, we suggest generating an auxiliary domain through data augmentation to enforce a new domain-level metric regularization. Experimental findings on public V2V benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios.

show abstract

Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving

Cited by 50 publications

References 183 publications

PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection

PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

Contact Info

Product

Resources

About