An experimental high performance radar system for highly automated driving

Meinl, Frank; Stolz, Martin; Kunert, Martin; Blume, Holger

doi:10.1109/icmim.2017.7918859

Cited by 39 publications

(14 citation statements)

References 8 publications

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“…Today's higher levels of automation often deploy sensor fusion concepts or semantic grids to detect also stationary objects [4]. The role of radar for driving automation has become increasingly important; with further developments towards higher resolution [5] and fully polarimetric devices [6], radar is considered a key sensor for autonomous driving [7]. Among the primary reasons for the success of radar are that it is more robust against adverse weather conditions compared to lidar or camera, and that it is able to measure the target's velocity via the Doppler-effect in addition to its range [8].…”

Section: Radar Sensors In Autonomous Drivingmentioning

confidence: 99%

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Holder

Rosenberger

Winner

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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The virtual validation of automated driving functions requires meaningful simulation models of environment perception sensors such as radar, lidar, and cameras. There does not yet exist an unrivaled standard for perception sensor models, and radar especially lacks modeling approaches that consistently produce realistic results. In this paper, we present measurements that exemplify challenges in the development of meaningful radar sensor models. We highlight three major challenges: multi-path propagation, separability, and sensitivity of radar cross section to the aspect angle. We also review previous work addressing these challenges and suggest further research directions towards meaningful automotive radar simulation models.

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Section: Radar Sensors In Autonomous Drivingmentioning

confidence: 99%

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Holder

Rosenberger

Winner

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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“…To evaluate the proposed approach, we use a case study of a LIDAR-based object detection system that forms part of the autonomous driving capabilities and adaptive cruise control system in a modern car [15], [16]. For our experiment, we have designed the system to receive sensor data frames over Ethernet and apply the Constant False Alarm Rate (CFAR) detection technique to determine the position and distance of objects.…”

Section: Methodsmentioning

confidence: 99%

A Smart Network Interface Approach for Distributed Applications on Xilinx Zynq SoCs

Shreejith

Cooke

Fahmy

2018

2018 28th International Conference on Field Programmable Logic and Applications (FPL)

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“…Similarly, if the vehicle is moving in a nonlinear fashion, i.e., a turn, v r,i depends not on the velocity vector, but instead on the yaw rate of the vehicle ω and the turning center (x ICR , y ICR ), as shown in Eq. (2).…”

Section: Velocity Profile Analysismentioning

confidence: 99%

“…In [1], a high-resolution radar is used to classify road users. In [2] an experimental radar system is shown, that is able to detect dozens of target points per road user in an automotive setting. These high performance radars pave the way for new radar applications in the field of advanced driver assistance systems and autonomous driving, by allowing for estimating the dimensions and orientation of road users in a scenario [3].…”

Section: Introductionmentioning

confidence: 99%

Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

Schlichenmaier¹,

Roos²,

Hügler³

et al. 2019

2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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As high resolution automotive radars become more common, so does their usage for next-generation functionalities like advanced driver assistant systems and autonomous driving. This creates the need for robust clustering techniques to distinguish among multiple extended objects like vehicles in the same scenario. One especially challenging scenario is that of separating two extended targets close to each other, each following its own trajectory. This paper proposes a clustering algorithm based on the analysis of the velocity profile to divide target points of multiple vehicles into sub-clusters. The theoretical background is explained and shown on simulation data. The algorithm is verified using radar measurements of two extended vehicular targets.

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An experimental high performance radar system for highly automated driving

Cited by 39 publications

References 8 publications

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

A Smart Network Interface Approach for Distributed Applications on Xilinx Zynq SoCs

Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

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