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Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night. In this study, we present and empirically assess the benefits of high-density LiDAR in low-light and dark conditions—a persistent challenge in VRU detection when compared to traditional RGB traffic cameras. Robust detection and tracking algorithms were utilized for analyzing VRU-to-vehicle and vehicle-to-vehicle interactions using the LiDAR data. The analysis explores the effectiveness of two DA metrics based on the i.e. Post Encroachment Time (PET) and Minimum Distance Safety Envelope (MDSE) formulations in identifying potentially unsafe scenarios for VRUs at the Tempe intersection. The codebase for the data pipeline, along with the high-density LiDAR dataset, has been open-sourced with the goal of benefiting the AV research community in the development of new methods for ensuring safety at urban traffic intersections.
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Enhancing Traffic Safety by Developing Vehicle Safety Envelope with Real Time Data Interface and Machine Learning Based Sensor Fusion Platform
The effectiveness of obstacle avoidance response safety systems such as ADAS, has demonstrated the necessity to optimally integrate and enhance these systems in vehicles in the interest of increasing the road safety of vehicle occupants and pedestrians. Vehicle-pedestrian clearance can be achieved with a model safety envelope based on distance sensors designed to keep a threshold between the ego-vehicle and pedestrians or objects in the traffic environment. More accurate, reliable and robust distance measurements are possible by the implementation of multi-sensor fusion. This work presents the structure of a machine learning based sensor fusion algorithm that can accurately detect a vehicle safety envelope with the use of a HC-SR04 ultrasonic sensor, SF11/C microLiDAR sensor, and a 2D RPLiDAR A3M1 sensor. Sensors for the vehicle safety envelope and ADAS were calibrated for optimal performance and integration with versatile vehicle-sensor platforms. Results for this work include a robust distance sensor fusion algorithm that can correctly sense obstacles from 0.05m to 0.5m on average by 94.33% when trained as individual networks per distance. When the algorithm is trained as a common network of all distances, it can correctly sense obstacles at the same distances on average by 96.95%. Results were measured based on the precision and accuracy of the sensors’ outputs by the time of activation of the safety response once a potential collision was detected. From the results of this work the platform has the potential to identify collision scenarios, warning the driver, and taking corrective action based on the coordinate at which the risk has been identified.
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- Award ID(s):
- 1950207
- PAR ID:
- 10414719
- Date Published:
- Journal Name:
- SAE Technical Paper Series
- Volume:
- 1
- ISSN:
- 0148-7191
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4271/2023-01-0053
