PRORETA 3: Comprehensive Driver Assistance by Safety Corridor and Cooperative Automation

Winner, Hermann; Lotz, Felix; Bauer, Eric; Konigorski, Ulrich; Schreier, Matthias; Adamy, Jürgen; Pfromm, Matthias; Bruder, Ralph; Lüke, Stefan; Cieler, Stephan

doi:10.1007/978-3-319-09840-1_19-1

Cited by 13 publications

(16 citation statements)

References 11 publications

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“…Afterwards, the test persons answered a questionnaire anonymously. On a 6 point grading scale (from 1: excellent; to 6: fail), the SC mode received 1.92 ± 0.93 points, the CA mode 1.87 ± 0.99 points, and the entire PRORETA 3 system 1.74 ± 0.86 points [274]. 3 Thus, the results of both modes and of the complete system do not differ significantly and underline the throughout positive system experience.…”

Section: Resultsmentioning

confidence: 83%

“…7.7. Before the results obtained from the test persons are summarized, the scenarios are first explained shortly based on [274] in the following, beginning with the SC mode scenarios. …”

Section: Driving Scenarios and Resultsmentioning

confidence: 99%

“…7.1, in the course of this thesis. For more detailed information, the reader is invited to consult the related articles [25,56,274,275] and dissertations [23,158,196].…”

Section: Introductionmentioning

confidence: 99%

“…• for potential field-based safety trajectory planning and lateral vehicle guidance with respect to elevated objects in PRORETA 3 [25,56,219,220,274,275], see Chapter 7.…”

mentioning

confidence: 99%

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Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.

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Section: Resultsmentioning

confidence: 83%

“…7.7. Before the results obtained from the test persons are summarized, the scenarios are first explained shortly based on [274] in the following, beginning with the SC mode scenarios. …”

Section: Driving Scenarios and Resultsmentioning

confidence: 99%

“…7.1, in the course of this thesis. For more detailed information, the reader is invited to consult the related articles [25,56,274,275] and dissertations [23,158,196].…”

Section: Introductionmentioning

confidence: 99%

“…• for potential field-based safety trajectory planning and lateral vehicle guidance with respect to elevated objects in PRORETA 3 [25,56,219,220,274,275], see Chapter 7.…”

mentioning

confidence: 99%

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Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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“…Radar sensors are a fundamental component of most autonomous vehicles. Their low price and robustness against bad weather and lighting conditions make them great companions for lidar and vision cameras (Hasch et al, 2012 ; Winner et al, 2014 ; Patole et al, 2017 ). Whereas, the algorithms in this field are typically implemented on graphical processing units (GPUs), field-programmable gate arrays (FPGAs), or application-specific integrated circuits (ASICs) (Lin et al, 2018 ), neuromorphic hardware (NHW) offers an efficient alternative environment (Furber et al, 2014 ; Davies et al, 2018 ; Sangwan and Hersam, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

et al. 2021

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The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural networks (SNNs). These systems are well-suited to recognize temporal patterns in data while maintaining a low energy consumption and offering highly parallel architectures for fast computation. However, the lack of effective algorithms for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem of radar signal processing by introducing a novel SNN that substitutes the discrete Fourier transform and constant false-alarm rate algorithm for raw radar data, where the weights and architecture of the SNN are derived from the original algorithms. We demonstrate that our proposed SNN can achieve competitive results compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.

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A Conceptual Safety Supervisor Definition and Evaluation Framework for Autonomous Systems

Feth

Schneider

Adler

2017

Lecture Notes in Computer Science

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PRORETA 3: Comprehensive Driver Assistance by Safety Corridor and Cooperative Automation

Cited by 13 publications

References 11 publications

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

A Conceptual Safety Supervisor Definition and Evaluation Framework for Autonomous Systems

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