Electronic sensing technologies for autonomous ground vehicles: A review

Ilas, C.

doi:10.1109/atee.2013.6563528

Cited by 68 publications

(34 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SRR has higher range resolution and wider field of view (FoV ±65°~± 80°), which is used for collision avoidance and object detection, and its typical applications include blind spot surveillance, parking assist and ACC support. MRR (typical FoV ±40°~±50°) is used for LCA; LRR has high gain and narrow beam for long detection range and accurate direction measurement, with smaller FoV around ±10º and more range resolution tolerance, which is mainly used for ACC, FCW, and AEB [3][4]. Key radar sensor manufacturers in the world include Bosch, Delphi, Continental, Denso, Valeo, ZF-TRW, etc.…”

Section: Automotive Radarmentioning

confidence: 99%

An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

et al. 2020

View full text Add to dashboard Cite

The mass production of autonomous vehicle is coming, thanks to the rapid progress of autonomous driving technology, especially the recent breakthroughs in LiDAR sensors, GPUs, and deep learning. Many automotive and IT companies represented by Waymo and GM are constantly promoting their advanced autonomous vehicles to hit public roads as early as possible. This paper systematically reviews the latest development and future trend of the autonomous vehicle technologies, discusses the extensive application of AI in ICV, and identifies the key problems and core challenges facing the commercialization of autonomous vehicle. Based on the review, it forecasts the prospects and conditions of autonomous vehicle’s mass production and points out the arduous, long-term and systematic nature of its development.

show abstract

Section: Automotive Radarmentioning

confidence: 99%

An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

et al. 2020

View full text Add to dashboard Cite

show abstract

“…With the introduction of autonomous vehicles, and the explosion of drones for civilian applications, light detection and ranging (LIDAR) uses for collision avoidance and topological measurement is becoming ubiquitous. [21][22][23][24] Proc. of SPIE Vol.…”

Section: Holography For Lidarmentioning

confidence: 99%

Holography for automotive applications: from HUD to LIDAR

Blanche

Bigler

Draper

et al. 2018

Optical Data Storage 2018: Industrial Optical Devices and Systems

View full text Add to dashboard Cite

Holography can offer unique solutions to the specific problems faced by automotive optical systems. Frequently, when possibilities have been exhausted using refractive and reflective designs, diffraction can come to the rescue by opening a new dimension to explore. Holographic optical elements (HOEs), for example, are thin film optics that can advantageously replace lenses, prisms, or mirrors. Head up display (HUD) and LIDAR for autonomous vehicles are two of the systems where our group have used HOEs to provide original answers to the limitations of classical optic. With HUD, HOEs address the problems of the limited field of view, and small eye box usually found in projection systems. Our approach is to recycle the light multiple times inside a waveguide so the combiner can be as large as the entire windshield. In this system, a hologram is used to inject a small image at one end of a waveguide, and another hologram is used to extract the image several times, providing an expanded eye box. In the case of LIDAR systems, non-mechanical beam scanning based on diffractive spatial light modulator (SLM), are only able to achieve an angular range of few degrees. We used multiplexed volume holograms (VH) to amplify the initial diffraction angle from the SLM to achieve up to 4π steradian coverage in a compact form factor.

show abstract

“…Perception algorithms for intelligent cars have been an important research topic for more than a decade. Significant progress has been achieved [1][2][3][4][5][6] and such algorithms are used for both autonomous car prototypes and for commercial driver assistance systems [4][5][6]. As is known [7,8], a video camera is the preferred sensor (sometimes integrated with a 2D laser [9]) due to its cost.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Ilas

2018

Computers

Self Cite

View full text Add to dashboard Cite

In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG) algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs), by considerably reducing the area (thus increasing the level of parallelism), while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.

show abstract

Electronic sensing technologies for autonomous ground vehicles: A review

Cited by 68 publications

References 14 publications

An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

Holography for automotive applications: from HUD to LIDAR

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Contact Info

Product

Resources

About