Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking

Schönemann, Valerij; Duschek, Mara; Winner, Hermann

doi:10.5220/0007689503430351

Cited by 3 publications

(1 citation statement)

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“…Fig. 1 sketches an application from automated valet parking [7], [8], where the video data from an infrastructure camera augments the in-vehicle sensors to obtain images from different directions helping to detect hidden objects. The video stream consists of a periodic sequence of camera frames.…”

Section: Introductionmentioning

confidence: 99%

A Middleware Protocol for Time-Critical Wireless Communication of Large Data Samples

Peeck

Mostl

Ishigooka³

et al. 2021

2021 IEEE Real-Time Systems Symposium (RTSS)

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We present a middleware-based protocol that reliably synchronizes large samples consisting of multiple frames efficiently and within application level QoS requirements over a lossy wireless channel. The protocol uses a custom retransmission scheme, exploiting the latency requirements on sample level for frame level scheduling. It can be integrated into the popular DDS middleware. We investigate some technical limits of such a protocol and compare it to existing error protocols in the software stack and in the wireless protocol and combinations thereof. The comparison is based on an Omnet++ simulation using an established wireless channel error model. For evaluation, we take a use case from automated valet parking where infrastructure data provided via a wireless link augments in-vehicle sensor data. The use case respects the related safety requirements. Results show that the application awareness of the presented protocol, significantly improves service availability by transmitting data efficiently in time even under higher frame error rates.

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Section: Introductionmentioning

confidence: 99%

A Middleware Protocol for Time-Critical Wireless Communication of Large Data Samples

Peeck

Mostl

Ishigooka³

et al. 2021

2021 IEEE Real-Time Systems Symposium (RTSS)

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Systematization of Relevant Road Users for the Evaluation of Autonomous Vehicle Perception

Philipp

Rehbein²,

Grün

et al. 2022

2022 IEEE International Systems Conference (SysCon)

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SHARD: Safety and Human Performance Analysis for Requirements in Detection

Mori,

Peters

2024

IEEE Trans. Intell. Veh.

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Automated driving requires reliable perception of the environment to ensure the safety of the driving task. One common perception task is 3D object detection, which aims at perceiving location and attributes of dynamic objects. This task is typically evaluated on different benchmark datasets, which each propose different metrics. However, these different metrics generally lack consistency and bear no relation to safety. Most notably, there is a lack of consistent definitions of pass/fail criteria for any given detection metric. In this work, the issue is addressed by systematically considering safety and human performance across different aspects of the object detection task. This approach yields interpretable detection metrics as well as thresholds for pass/fail criteria. Furthermore, a validation approach leveraging a prediction network is introduced and successfully applied to the requirements. A comparison of existing detectors shows that current perception algorithms exhibit failures for a majority of objects on the nuScenes dataset. Therefore, the results indicate the necessity of explicit safety consideration in the development of perception algorithms for the automated driving task.

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Maneuver-based Adaptive Safety Zone for Infrastructure-Supported Automated Valet Parking

Cited by 3 publications

References 0 publications

A Middleware Protocol for Time-Critical Wireless Communication of Large Data Samples

A Middleware Protocol for Time-Critical Wireless Communication of Large Data Samples

Systematization of Relevant Road Users for the Evaluation of Autonomous Vehicle Perception

SHARD: Safety and Human Performance Analysis for Requirements in Detection

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