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Automated vehicles (AVs) are of great potential in reducing crashes on the road. However, it is still complicated to eliminate all the possible accidents, especially those with vulnerable road users (VRUs), who are among the greater risk than vehicle occupants in traffic accidents. Thus, in this paper, we conducted a systematic review of safety-critical scenarios between AVs and VRUs. We identified 39 papers in the literature and typical safety-critical scenarios between AVs and VRUs. They were further divided into three categories, including human factors, environmental factors, and vehicle factors. We then discussed the development, challenges, and possible solutions for each category. In order to further improve the safety of VRUs when interacting with AVs, multiple stakeholders should work together to 1) improve AI and sensor technologies and vehicle automation, 2) redesign the current transportation infrastructure, 3) design effective communication technologies and interfaces between vehicles and between vehicles and VRUs, and 4) design effective simulation and testing methods to support and evaluate both infrastructure and technologies.

Micro-mobility is promising to contribute to sustainable cities in the future with its efficiency and low cost. To better design such a sustainable future, it is necessary to understand the trends and challenges. Thus, we examined people's opinions on micro-mobility in the US and the EU using Tweets. We used topic modeling based on advanced natural language processing techniques and categorized the data into seven topics: promotion and service, mobility, technical features, acceptance, recreation, infrastructure and regulations. Furthermore, using sentiment analysis, we investigated people's positive and negative attitudes towards specific aspects of these topics and compared the patterns of the trends and challenges in the US and the EU. We found that 1) promotion and service included the majority of Twitter discussions in the both regions, 2) the EU had more positive opinions than the US, 3) micro-mobility devices were more widely used for utilitarian mobility and recreational purposes in the EU than in the US, and 4) compared to the EU, people in the US had many more concerns related to infrastructure and regulation issues. These findings help us understand the trends and challenges and prioritize different aspects in micro-mobility to improve their safety and experience across the two areas for designing a more sustainable micro-mobility future., Comment: 33 pages, 4 figures

Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.

Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs. However, most existing SSMs were developed based on the assumption that road users would maintain constant velocity and direction. Risk estimations based on this assumption are less unstable, more likely to be exaggerated, and unable to capture the evasive maneuvers of drivers. Considering the limitations among existing SSMs, this study proposes a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework loosen restrictions of constant speed by predicting trajectories using a Gaussian Process Regression and accounts for the different possible driver maneuvers with a Random Forest model. Real-world LiDAR data collected at an intersection was used to evaluate the performance of the proposed framework. The newly developed framework is able to identify all pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed framework provides a more stable risk estimation and captures the evasive maneuvers of vehicles. Moreover, the proposed framework does not require expensive computation resources, which makes it an ideal choice for real-time proactive pedestrian safety solutions at intersections.

The public's attitudes play a critical role in the acceptance, purchase, use, and research and development of autonomous vehicles (AVs). To date, the public's attitudes towards AVs were mostly estimated through traditional survey data with high labor costs and a low quantity of samples, which also might be one of the reasons why the influencing factors on the public's attitudes of AVs have not been studied from multiple aspects in a comprehensive way yet. To address the issue, this study aims to propose a method by using large-scale social media data to investigate key factors that affect the public's attitudes and acceptance of AVs. A total of 954,151 Twitter data related to AVs and 53 candidate independent variables from seven categories were extracted using the web scraping method. Then, sentiment analysis was used to measure the public attitudes towards AVs by calculating sentiment scores. Random forests algorithm was employed to preliminarily select candidate independent variables according to their importance, while a linear mixed model was performed to explore the impacting factors considering the unobserved heterogeneities caused by the subjectivity level of tweets. The results showed that the overall attitude of the public on AVs was slightly optimistic. Factors like "drunk", "blind spot", and "mobility" had the largest impacts on public attitudes. In addition, people were more likely to express positive feelings when talking about words such as "lidar" and "Tesla" that relate to high technologies. Conversely, factors such as "COVID-19", "pedestrian", "sleepy", and "highway" were found to have significantly negative effects on the public's attitudes. The findings of this study are beneficial for the development of AV technologies, the guidelines for AV-related policy formulation, and the public's understanding and acceptance of AVs., Comment: 22 pages, 5 figures

Influencing factors on crashes involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses between influencing factors on crashes of AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crashes under autonomous and conventional driving modes. This study obtained 154 publicly available autonomous vehicle crash data (70 for the autonomous driving mode and 84 for the conventional driving mode), and 36 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash type and severity under both driving modes. The results showed that some factors affected both driving modes, but their degrees were different. For example, the presence of turning movement had a greater impact on the crash severity under the conventional driving mode, while the presence of turning movement led to a larger decrease in the likelihood of rear-end crashes under the autonomous driving mode. More influencing factors only had a significant impact on one of the driving modes. For example, in the autonomous driving mode, two sidewalks decreased the severity of crashes, and on-street parking was positively associated with rear-end crashes, but they were not significant in the conventional driving mode. This study could contribute to the understanding and development of autonomous driving systems and the better coordination between autonomous driving and conventional driving., Comment: 42 pages,10 figures

In this study, we investigated the effectiveness and user acceptance of three external interaction modalities (i.e., visual, auditory, and visual+auditory) in promoting communications between automated vehicle systems (AVS) and pedestrians at a crosswalk through a large number of combined designs. For this purpose, an online survey was designed and distributed to 68 participants. All participants reported their overall preferences for safety, comfort, trust, ease of understanding, usability, and acceptance towards the systems. Results showed that the visual+auditory interaction modality was the mostly preferred, followed by the visual interaction modality and then the auditory one. We also tested different visual and auditory interaction methods, and found that "Pedestrian silhouette on the front of the vehicle" was the best preferred option while middle-aged participants liked "Chime" much better than young participants though it was overall better preferred than others. Finally, communication between the AVS and pedestrians' phones was not well received due to privacy concerns. These results provided important interface design recommendations in identifying better combination of visual and auditory designs and therefore improving AVS communicating their intention with pedestrians.

The observed heaviest anti-nucleus so far is $^4\overline{H}e$ which is found in relativistic heavy ion collider in 2011. The yield of $^4\overline{L}i$ is four times bigger than that of $^4\overline{H}e$ according to the thermal model. From previous scattering experiment, we know that the $^4Li$ has a very short life time about $1.197\times10^{-22}s$. It decays into $^3{H}e$ and ${p}$. In experiment, the correlation function of $\overline{p}-^3\overline{H}e$ will offer us a method to observe $^4\overline{L}i$ by CPT symmetry. In this paper, we use the blast-wave model and Lednicky-Lyuboshitz analytical model to obtain a prediction of the correlation function of $\overline{p}-{}^3\overline{H}e$ with/without $^4\overline{L}i$ decay in Au + Au collisions at $\sqrt{S_{NN}}$ = 200 GeV. The correlation function with $^4\overline{L}i$ decay is found to exhibit a peak at $k^* \approx$ 0.073GeV/$c$. The results offer a guide for the experimental search for $^4\overline{L}i$ in relativistic heavy ion collision., Comment: 7 pages, 6 figures

This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

The behaviors of heavy vehicles drivers in mandatory and discretionary lane changes are analyzed in this paper. 640 mandatory and 2,035 discretionary lane change events were extracted from a naturalistic driving database. Variations in gap acceptance and lane change duration were investigated. Statistical analysis showed that mandatory lane changes are more aggressive in gap acceptance and lane change execution than discretionary lane changes. The results can be used for microscopic simulations, and design and evaluation of driver-assistant systems., Comment: Published in the 12th International Symposium on Advanced Vehicle Control, AVEC'14

Automated vehicles (AVs) must be evaluated thoroughly before their release and deployment. A widely-used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time-consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the primary other vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in non-accelerated cases can be accurately estimated. The Cross Entropy method is used to recursively search for the optimal skewing parameters. The frequencies of occurrence of conflicts, crashes and injuries are estimated for a modeled automated vehicle, and the achieved accelerated rate is around 2,000 to 20,000. In other words, in the accelerated simulations, driving for 1,000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to reduce greatly the development and validation time for AVs., Comment: submitted to IEEE ITSC