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1. Investigating Traffic Crashes Involving Autonomous Vehicles

   Torres, Jesus (May 2021, IISE Annual Conference Proceedings)

   A. Ghate, K. Krishnaiyer (Ed.)

   Deaths due to road traffic accidents are one of the leading causes of death in the United States. Furthermore, the economic impact of road traffic accidents accounts for about 3% of the United States' annual gross domestic product (GDP). In the past decade, extensive research has focused on autonomous vehicles (AVs). This technology is said to help prevent traffic accidents while promoting road traffic safety. This study aims to investigate the safety performance of AVs and identify the significant risk factors associated with the AV collisions. The study considers more than 200 crashes involving AVs and includes vehicle factors, environmental factors, collision type and crash severity. Multinomial logistic regression was conducted with collision type. The results showed no statistically significant risk factors to crash severity. However, movement preceding to collision contributes significantly to collision type. When both vehicles are moving, there's a higher likelihood of an angled collision, 47% to be exact. The other scenario which demonstrates a high probability of an angled collision is when the AV vehicle is not moving while the other is moving. The highest probability for a rear-end collision to occur is when the AV vehicle is not moving while the other is moving. This scenario makes up 55% of the entire rear-end collisions. As for the second-highest proportion, both vehicles moving, it consists of 42%. The research shall help reduce AV involved collisions and increase driving safety. 

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2. Towards Safe AI: Sandboxing DNNs-Based Controllers in Stochastic Games

   https://doi.org/10.1609/aaai.v37i12.26789  

   Zhong, Bingzhuo; Cao, Hongpeng; Zamani, Majid; Caccamo, Marco (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Nowadays, AI-based techniques, such as deep neural networks (DNNs), are widely deployed in autonomous systems for complex mission requirements (e.g., motion planning in robotics). However, DNNs-based controllers are typically very complex, and it is very hard to formally verify their correctness, potentially causing severe risks for safety-critical autonomous systems. In this paper, we propose a construction scheme for a so-called Safe-visor architecture to sandbox DNNs-based controllers. Particularly, we consider the construction under a stochastic game framework to provide a system-level safety guarantee which is robust to noises and disturbances. A supervisor is built to check the control inputs provided by a DNNs-based controller and decide whether to accept them. Meanwhile, a safety advisor is running in parallel to provide fallback control inputs in case the DNN-based controller is rejected. We demonstrate the proposed approaches on a quadrotor employing an unverified DNNs-based controller. 

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3. Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures

   https://doi.org/10.3390/futuretransp1020022  

   Shourov, Chowdhury Erfan; Sarkar, Mahasweta; Jahangiri, Arash; Paolini, Christopher (September 2021, Future Transportation)

   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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4. Theory and implementation of dynamic watermarking for cybersecurity of advanced transportation systems

   Ko, Woo-Hyun; Satchidanandan, Bharadwaj; and Kumar, P.R. (October 2016, 2016 IEEE Conference on Communications and Network Security (CNS))

   We consider a prototypical intelligent transportation system with a control law that is specifically designed to avoid collisions. We experimentally demonstrate that, nevertheless, an attack on a position sensor can result in collisions between vehicles. This is a consequence of the feeding of malicious sensor measurements to the controller and the collision avoidance module built into the system. This is an instance of the broader concern of cybersecurity vulnerabilities opened up by the increasing integration of critical physical infrastructures with the cyber system. We consider a solution based on “dynamic watermarking” of signals to detect and stop such attacks on cyber-physical systems. We show how dynamic watermarking can handle nonlinearities arising in vehicular models. We then experimentally demonstrate that employing this nonlinear extension indeed restores the property of collision freedom even in the presence of attacks. 

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5. Risk-Aware Model Predictive Path Integral Control Using Conditional Value-at-Risk

   https://doi.org/10.1109/ICRA48891.2023.10161100  

   Yin, Ji; Zhang, Zhiyuan; Tsiotras, Panagiotis (May 2023, IEEE International Conference on Robotics and Automation)

   risk-aware planning; Conditional Value-at-Risk; Model Predictive Control; Model Predictive Path Integral (Ed.)

   In this paper, we present a novel Model Predictive Control method for autonomous robot planning and control subject to arbitrary forms of uncertainty. The proposed Risk- Aware Model Predictive Path Integral (RA-MPPI) control utilizes the Conditional Value-at-Risk (CVaR) measure to generate optimal control actions for safety-critical robotic applications. Different from most existing Stochastic MPCs and CVaR optimization methods that linearize the original dynamics and formulate control tasks as convex programs, the proposed method directly uses the original dynamics without restricting the form of the cost functions or the noise. We apply the novel RA-MPPI controller to an autonomous vehicle to perform aggressive driving maneuvers in cluttered environments. Our simulations and experiments show that the proposed RA-MPPI controller can achieve similar lap times with the baseline MPPI controller while encountering significantly fewer collisions. The proposed controller performs online computation at an update frequency of up to 80 Hz, utilizing modern Graphics Processing Units (GPUs) to multi-thread the generation of trajectories as well as the CVaR values. 

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