Sensing and Machine Learning for Automotive Perception: A Review

Pandharipande, Ashish; Cheng, Chih-Hong; Dauwels, Justin; Gürbüz, Sevgi Zübeyde; Ibañez‐Guzmán, Javier; Li, Guofa; Piazzoni, Andrea; Wang, Pu; Santra, Avik

doi:10.1109/jsen.2023.3262134

Cited by 38 publications

(4 citation statements)

References 164 publications

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“…Environment perception lays the foundation for motion planning and control systems of unmanned robots and ground vehicles 1 , 2 . The progress of autonomous vehicle (AV) perception is always promoted by the emergence of large-scale datasets.…”

Section: Background and Summarymentioning

confidence: 99%

A road surface reconstruction dataset for autonomous driving

Zhao,

Xie,

Ding

et al. 2024

Sci Data

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Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

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Section: Background and Summarymentioning

confidence: 99%

A road surface reconstruction dataset for autonomous driving

Zhao,

Xie,

Ding

et al. 2024

Sci Data

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“…Furthermore, these techniques can generalize from learned data to new, unseen situations, making them robust to the varied and changing conditions in ITSs. Moreover, as previously mentioned, RL is specifically well suited to handle the optimization problems that lie at the heart of improving energy efficiency and reducing emissions (Pandharipande et al 2023). RL can learn from interaction with the environment to make sequences of decisions that maximize a reward function, such as minimizing energy use or emissions.…”

Section: Promoting Enhanced Eer In Itss Through Aimentioning

confidence: 99%

Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization

Rinchi

Elzanaty

Alsharoa

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.

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“…Pandharipande et al in [20], studied the efficiency of the perception process and the movement safety of an autonomous vehicle for various types of sensors in the form of camera, radar and lidar and with related data processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving

Lisowski

2024

Electronics

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The analysis of the state of the literature in the field of methods of perception and control of the movement of autonomous vehicles shows the possibilities of improving them by using an artificial neural network to generate domains of prohibited maneuvers of passing objects, contributing to increasing the safety of autonomous driving in various real conditions of the surrounding environment. This article concerns radar perception, which involves receiving information about the movement of many autonomous objects, then identifying and assigning them a collision risk and preparing a maneuvering response. In the identification process, each object is assigned a domain generated by a previously trained neural network. The size of the domain is proportional to the risk of collisions and distance changes during autonomous driving. Then, an optimal trajectory is determined from among the possible safe paths, ensuring control in a minimum of time. The presented solution to the radar perception task was illustrated with a computer simulation of autonomous driving in a situation of passing many objects. The main achievements presented in this article are the synthesis of a radar perception algorithm mapping the neural domains of autonomous objects characterizing their collision risk and the assessment of the degree of radar perception on the example of multi-object autonomous driving simulation.

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Sensing and Machine Learning for Automotive Perception: A Review

Cited by 38 publications

References 164 publications

A road surface reconstruction dataset for autonomous driving

A road surface reconstruction dataset for autonomous driving

Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization

Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving

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