Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, due to differences in their architectures and based on the advantages and disadvantages of each model, both models were individually used to perform two different set of tasks. Due to improved accuracy, the Faster R-CNN model was used to automate the calculation of post encroachment time, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, the Single Shot Multibox MobileNet V1 model was used. An outcome of this work is a model that can be deployed on low-cost, small-footprint mobile and IoT devices at traffic intersections with existing cameras to perform on-device inferencing for in situ Surrogate Safety Measurement (SSM), such as Time-To-Collision (TTC) and Post Encroachment Time (PET). SSM values that exceed a hazard threshold can be published to an Message Queuing Telemetry Transport (MQTT) broker, where messages are received by an intersection traffic signal controller for real-time signal adjustment, thus contributing to state-of-the-art vehicle and pedestrian safety at hazard-prone intersections.
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Elastic band based pedestrian collision avoidance using V2X communication
This paper is on a pedestrian collision warning and avoidance system for road vehicles based on V2X communication. In cases where the presence and location of a pedestrian or group of pedestrians cannot be determined using line-of-sight sensors like camera, radar and lidar, signals from pedestrians' smartphone apps are used to detect and localize them relative to the road vehicle through the DSRC radio used for V2X communication. A hardware-in-the-loop setup using a validated automated driving vehicle model in the high fidelity vehicle dynamics simulation program Carsim Real Time with Sensors and Traffic is used along with two DSRC modems emulating the vehicle and pedestrian communications in the development and initial experimental testing of this method. The vehicle either stops or, if possible, goes around the pedestrians in a socially acceptable manner. The elastic band method is used to locally modify the vehicle trajectory in real time when pedestrians are detected on the nearby path of the vehicle. The effectiveness of the proposed method is demonstrated using hardware-in-the-loop simulations.
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- Award ID(s):
- 1640308
- NSF-PAR ID:
- 10076468
- Date Published:
- Journal Name:
- 2017 IEEE Intelligent Vehicles Symposium (IV)
- Page Range / eLocation ID:
- 270 to 276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency communication-enabled device with an activated pedestrian safety application in their hand-held device all the time. Because of this, multiple traffic cameras at a signalized intersection could be used to accurately detect and locate pedestrians using deep learning, and broadcast safety alerts related to pedestrians to warn connected and automated vehicles around signalized intersections. However, the unavailability of high-performance roadside computing infrastructure and the limited network bandwidth between traffic cameras and the computing infrastructure limits the ability of real-time data streaming and processing for pedestrian detection. In this paper, we describe an edge computing-based real-time pedestrian detection strategy that combines a pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining high pedestrian detection accuracy. We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined pedestrian detection accuracy. The performance of the pedestrian detection strategy is measured in relation to pedestrian classification accuracy with varying peak signal-to-noise ratios. The analyses reveal that we detect pedestrians by maintaining a defined detection accuracy with a peak signal-to-noise ratio 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31× reduction.more » « less
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Objective This study used a virtual environment to examine how older and younger pedestrians responded to simulated augmented reality (AR) overlays that indicated the crossability of gaps in a continuous stream of traffic.
Background Older adults represent a vulnerable group of pedestrians. AR has the potential to make the task of street-crossing safer and easier for older adults.
Method We used an immersive virtual environment to conduct a study with age group and condition as between-subjects factors. In the control condition, older and younger participants crossed a continuous stream of traffic without simulated AR overlays. In the AR condition, older and younger participants crossed with simulated AR overlays signaling whether gaps between vehicles were safe or unsafe to cross. Participants were subsequently interviewed about their experience.
Results We found that participants were more selective in their crossing decisions and took safer gaps in the AR condition as compared to the control condition. Older adult participants also reported reduced mental and physical demand in the AR condition compared to the control condition.
Conclusion AR overlays that display the crossability of gaps between vehicles have the potential to make street-crossing safer and easier for older adults. Additional research is needed in more complex real-world scenarios to further examine how AR overlays impact pedestrian behavior.
Application With rapid advances in autonomous vehicle and vehicle-to-pedestrian communication technologies, it is critical to study how pedestrians can be better supported. Our research provides key insights for ways to improve pedestrian safety applications using emerging technologies like AR.
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Abstract Vehicle‐to‐Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non‐line‐of‐sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context‐Aware Target Classification (CA‐TC) module coupled with a hybrid learning‐based predictive modeling technique for CVS systems is proposed. The CA‐TC consists of two modules: a Context‐Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA‐TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real‐world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on‐the‐fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.
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more » « lessThe 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.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IVS.2017.7995731
