Lyft 3D object detection for autonomous vehicles

Mandal, Sampurna; Biswas, Swagatam; Balaş, Valentina E.; Shaw, Rabindra Nath; Ghosh, Ankush

doi:10.1016/b978-0-323-85498-6.00003-4

Cited by 21 publications

(2 citation statements)

References 9 publications

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“…World-famous competitions for object detection are the VOC PASCAL challenge [65], COCO [66], ImageNet object detection challenge [67], Google open images challenge [68] and Lyft [69]. All these competitions provide their code to calculate average precision, or mean AP, but the Lyft 3D object detection for autonomous vehicles challenge uses the AP averaged over 10 different thresholds, the so-called AP@50:5:95 metric.…”

Section: F Object Detection Competitionsmentioning

confidence: 99%

Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

Karri,

da Silva,

Correia

2023

IEEE Access

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Perception algorithms are essential for autonomous or semi-autonomous vehicles to perceive the semantics of their surroundings, including object detection, panoptic segmentation, and tracking. Decision-making in case of safety-critical situations, like autonomous emergency braking and collision avoidance, relies on the outputs of these algorithms. This makes it essential to correctly assess such perception systems before their deployment and to monitor their performance when in use. It is difficult to test and validate these systems, particularly at runtime, due to the high-level and complex representations of their outputs. This paper presents an overview of different existing metrics used for the evaluation of LiDAR-based perception systems, emphasizing particularly object detection and tracking algorithms due to their importance in the final perception outcome. Along with generally used metrics, we also discuss the impact of Planning KL-Divergence (PKL), Timed Quality Temporal Logic (TQTL), and Spatio-temporal Quality Logic (STQL) metrics on object detection algorithms. In the case of panoptic segmentation, Panoptic Quality (PQ) and Parsing Covering (PC) metrics are analysed resorting to some pretrained models. Finally, it addresses the application of diverse metrics to evaluate different pretrained models with the respective perception algorithms on publicly available datasets. Besides the identification of the various metrics being proposed, their performance and influence on models are also assessed after conducting new tests or reproducing the experimental results of the reference under consideration.

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Section: F Object Detection Competitionsmentioning

confidence: 99%

Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

Karri,

da Silva,

Correia

2023

IEEE Access

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“…Thereby, real-time object recognition is attained because of the decreased communication latency. Mandal et al [17] focus on identifying three-dimensional objects with three-dimensional bounding boxes that come in the extent of a camera or AGV LiDAR. The primary goal is to utilize DL methods for training the LiDAR and camera images and estimate the confidence score for all the models.…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

et al. 2022

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Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures.

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Real-Time 3D Reconstruction for Mixed Reality Telepresence Using Multiple Depth Sensors

Ishigaki

Ismail

2023

Communications in Computer and Information Science

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Lyft 3D object detection for autonomous vehicles

Cited by 21 publications

References 9 publications

Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

Real-Time 3D Reconstruction for Mixed Reality Telepresence Using Multiple Depth Sensors

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