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1. Evaluating the mobility performance of autonomous vehicles at a signalized traffic intersection

   https://doi.org/10.1117/12.2606614  

   Ahmad, Salman ; Rogers, Joshua ; Nelson, Martha ; Huang, Ying ; Ajmera, Beena ; Lu, Pan ( April 2022 , Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022)

   Zonta, Daniele ; Su, Zhongqing ; Glisic, Branko (Ed.)

   Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance. 

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2. 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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3. Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction

   https://doi.org/10.1145/3610904  

   Tabatabaie, Mahan ; He, Suining ; Shin, Kang G. ( September 2023 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both spoken language (e.g., human textual annotations) and unspoken language (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design.

   To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken languages, we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants.

    

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4. UTT: A Conceptual Model to Guide the Universal Design of Autonomous Vehicles

   Carvalho, S ; Ahire, S ; Huff, E ; Brinkley, J. ( January 2020 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Autonomous vehicles (AVs) are closer to becoming a reality in changing the landscape of commercial and personal transportation. The launch of these vehicles come with the promise of improved road safety, reduced traffic fatalities, and enhanced mobility. However, there are questions as to whether the design of AVs will meet the needs of everyone, including people with disabilities and older adults. We argue that there exists no conceptual model that guide sthe inclusive design of autonomous vehicles to benefit all intended users. This paper proposes such a model, called the User Transportation-Activity Technology (UTT) model, which supports the inclusive design of AVs. We present a review of current models of assistive technology design and their drawbacks followed by an introduction of the UTT model and its application in AV design. This paper may benefit researchers, designers, and developers of autonomous vehicles interested in addressing accessible design issues in such vehicles. 

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5. Autonomous Driving Security: A Comprehensive Threat Model of Attacks and Mitigation Strategies

   Hoque, Mohammad Aminul ; Hasan, Ragib ( July 2022 , Proceedings of the 8th IEEE World Forum on Internet of Things (WF-IOT))

   Autonomous vehicles (AVs) are envisioned to enhance safety and efficiency on the road, increase productivity, and positively impact the urban transportation system. Due to recent developments in autonomous driving (AD) technology, AVs have started moving on the road. However, this promising technology has many unique security challenges that have the potential to cause traffic accidents. Though some researchers have exploited and addressed specific security issues in AD, there is a lack of a systematic approach to designing security solutions using a comprehensive threat model. A threat model analyzes and identifies potential threats and vulnerabilities. It also identifies the attacker model and proposes mitigation strategies based on known security solutions. As an emerging cyber-physical system, the AD system requires a well-designed threat model to understand the security threats and design solutions. This paper explores security issues in the AD system and analyzes the threat model using the STRIDE threat modeling process. We posit that our threat model-based analysis will help improve AVs' security and guide researchers toward developing secure AVs. 

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