Radar-on-Chip/in-Package in Autonomous Driving Vehicles and Intelligent Transport Systems: Opportunities and Challenges

Saponara, Sergio; Greco, Maria; Gini, Fulvio

doi:10.1109/msp.2019.2909074

Cited by 58 publications

(27 citation statements)

References 44 publications

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“…Autonomous driving is a safety critical application, as specified also in functional safety standards like ISO26262, with strict requirements in terms of real-time (both throughput and latency) [1,2]. In Levels 1 and 2 of the SAE autonomous driving scale [1] just an assistance to human driver is needed. Hence, signal processing based on deterministic algorithms is still enough, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, signal processing based on deterministic algorithms is still enough, e.g. FFT-based processing of Frequency Modulated Continuous Wave Radar (FMCW) as done in [1]. Instead, for high autonomous driving levels, from L3 to L5, the complexity of the scenario and the needs of signal processing are very high, not only for sensing, but also for localization, navigation, decision and actuation.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, for high autonomous driving levels, from L3 to L5, the complexity of the scenario and the needs of signal processing are very high, not only for sensing, but also for localization, navigation, decision and actuation. As consequence, in recent state-of-art Machine Learning (ML) signal processing is proposed to be used on-board of vehicles [1][2][3][4]. ML approaches have reached the state-of-art in several signal processing domains [4][5][6][7] like image processing, segmentation, classification and broader computer vision.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Arithmetics to Accelerate Machine Learning Classifiers in Autonomous Driving Applications

Cococcioni

Rossi

Ruffaldi³

et al. 2019

2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

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Autonomous driving techniques frequently need the clustering and the classification of data coming from several input sensors, like cameras, radar and lidars. These sub-tasks need to be implemented0020in real-time in embedded on-board computing units. The trend for data classification and clustering in the signal processing community is moving towards machine learning (ML) algorithms. One of them, which plays a central role, is the k-nearest neighbors (k-NN) algorithm. To meet stringent requirements in terms of real-time computing capability and circuit/memory complexity, ML accelerators are needed. Innovation is required in terms of computing arithmetic since classic integer numbers lead to low classification accuracy with respect to the needs of safety critical applications like autonomous driving. Instead, floating numbers require too much circuit and memory. To overcome these issues the paper shows that the use of a new format, called Posit, implemented in a new cppPosit software library, can lead to a k-NN implementation having the same accuracy of floats, but with halved bit-size. This means that a Posit Processing Unit (PPU) reduces by a factor higher than 2 the data transfer and storage complexity of ML accelerators. We also prove that a LUTbased complete tabulated implementation of a PPU for a 8-bit requires just 64 kB storage size, compliant with memoryconstrained devices.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Arithmetics to Accelerate Machine Learning Classifiers in Autonomous Driving Applications

Cococcioni

Rossi

Ruffaldi³

et al. 2019

2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

Self Cite

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“…where Y ∈ C M N ×Q contains the received data after matched filtering, Z is the spatially and temporally white additive noise, C T ∈ C P ×Q contains the reflection coefficients of P targets corresponding to Q pulses, and A ∈ C M ×P and B ∈ C N ×P denote the respective steering matrices for transmit and receive arrays. Arranging the matched-filter outputs as a tensor Y ∈ C M ×N ×Q and following the definitions of the matrix unfoldings in Section II-B2, it can be observed that model (52) represents the PARAFAC decomposition. As such, target parameter estimation can proceed within the PARAFAC framework.…”

Section: A Tensor Techniques For Target Parameter Estimation In Mimo Radarmentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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“…Any sensor that goes on the hard hat must be small, lightweight, and robust to rugged environments like construction or mining. Recent advances in microelectronics and signal processing have provided us with minimal, low cost and high-performance Doppler radars [56][57][58]. Our idea is to mount CW radars on hard hats and to sense the surroundings.…”

Section: Smart Hard Hatsmentioning

confidence: 99%

Smart Hard Hats: Sensing for Worker’s Safety in Dangerous Work Environments

E¹

2020

Preprint

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Accidents and mishaps in industrial environments like construction, mining, and transport are rampant - mainly due to human negligence and improper monitoring of the workplace. In this paper, we address the safety of workers operating in dangerous environments by improving their situational awareness. According to Occupational health and safety rules, everyone must wear hard hats while on site. Our main idea is to make the hard hats smart by incorporating miniature-sized Doppler radars sensing the users’ surroundings. These Doppler radars are lightweight, rugged, and consume low-power compared to vision-based solutions. This paper discusses the observability of range from Doppler frequency measurements and the magnitude of estimation errors introduced by the human head, walking, and working motions. We present the framework to estimate the position of walls and targets surrounding the worker. For testing, we simulated an indoor environment with randomly moving workers. Experiments showed that once observability conditions are met, human head and walking movements can be handled through added noise in the system. We also present an innovative idea of using two Doppler radars to obtain the estimators’ initial estimates, reducing the estimation error to less than 5cm and convergence time by more than 80

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Radar-on-Chip/in-Package in Autonomous Driving Vehicles and Intelligent Transport Systems: Opportunities and Challenges

Cited by 58 publications

References 44 publications

Novel Arithmetics to Accelerate Machine Learning Classifiers in Autonomous Driving Applications

Novel Arithmetics to Accelerate Machine Learning Classifiers in Autonomous Driving Applications

Tensor Decompositions in Wireless Communications and MIMO Radar

Smart Hard Hats: Sensing for Worker’s Safety in Dangerous Work Environments

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