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Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types. [ABSTRACT FROM AUTHOR]

Recent studies suggest that advances in rear seat occupant protection are trailing while frontal crash prevention technologies have disproportionately improved front seat occupant safety. Therefore, the first objective of this study is to identify the safest seat for children by estimating injury severity by seat position using current crash data. The second objective of this study is to demonstrate that Level-2 and above (i.e., L2+) AVs will be significantly different from current vehicles regarding child injury severity, and therefore it is essential to find the safest seat for children in L2+ AVs. This study utilized data from the National Automotive Sampling System (NASS) to estimate crash injury severity by seat position in children. This study used the Injury Severity Score (ISS) as its measure of crash severity. The mean ISS for restrained children sitting in the front passenger seat was 0.494 (for model year > 2000 vehicles). The mean ISSs for second-row left and second-row right seats were 0.374 and 0.322, respectively. The second-row middle seat had 162, 98, and 71% lower mean ISSs than the front passenger, second-row left, and second-row right seats, respectively. Overall, in both restrained and unrestrained scenarios, the safest seat for a child was the second-row middle seat. [ABSTRACT FROM AUTHOR]

In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on "self-driving/autonomous" technology increased by 32 percentage points (from 14% to 46%). The compound scores of "pedestrian crash", "Uber", and "Tesla" keywords saw a 6% decrease while "self-driving/autonomous" recorded the highest change with an 11% decrease. Before the Uber-incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception. • Public perceptions play a crucial role in wide adoption of Autonomous Vehicles (AVs). • Sentiment analysis on 1.7 million tweets was conducted to study public's perceptions. • Tweets on AVs are more negative after their involvement in fatal crashes. • Safety facts on AVs should be publicized to curtail the negativity on this technology. [ABSTRACT FROM AUTHOR]

Public perceptions play a crucial role in wider adoption of autonomous vehicles (AVs). This paper aims to make two contributions to the understanding of public attitudes toward AVs. First, we explore opinions regarding the perceived benefits and challenges of AVs among vulnerable road users – in particular, pedestrians and bicyclists. Second, the paper evaluated whether interaction experiences with AVs influence perceptions among vulnerable road users. To explore this, we examined survey data collected by Bike PGH, a Pittsburgh based organization involved in programs to promote safe mobility options for road users. Analysis of the data revealed that respondents with direct experience interacting with AVs reported significantly higher expectations of the safety benefits of the transition to AVs than respondents with no AV interaction experience. This finding did not differ across pedestrian and bicyclist respondents. The results of this study indicate that as the public increasingly interacts with AVs, their attitudes toward the technology are more likely to be positive. Thus, this study recommends that policy makers should provide the opportunities for the public to have interaction experience with AVs. The opportunities can be provided through legislation that allows auto manufacturers and technology industries to operate and test AVs on public roads. This interactive experience will positively affect people's perceptions and help in wider adoption of AV technology. • Public perceptions play a crucial role in wider adoption of autonomous vehicles (AVs). • As the public increasingly interacts with AVs, their attitudes toward the technology are more likely to be positive. • We recommend policy makers to pass legislation that will authorize testing and operation of AVs on public roads. • Interaction experience with AVs will positively affect people's perceptions and help in wider adoption of the technology. [ABSTRACT FROM AUTHOR]

Motor vehicles with advanced safety technologies are rapidly entering the marketplace and the impact of new features are transforming safety on roadways. Among the several safety related technologies currently available in the market, this paper aims to forecast the reduction in crashes with gradual adoption of vehicles with lane departure prevention (LDP) technology. Crash data for the state of Alabama from 2014 to 2016 were used to evaluate the safety benefits of LDP technology. In Alabama, 75% of single-vehicle crashes are the result of lane departure. A 20% effective LDP system implies, whereby an LDP system would prevent a vehicle from exiting a roadway on 20% of applicable instances, would reduce 2.7% and 16.4% of the relevant single-vehicle lane departure (SVLD) crashes by 2020 and 2045 respectively. With increase in the effectiveness of the technology, a greater reduction in crashes was observed. With 100% effectiveness, this technology can prevent 66.5% of SVLD crashes by year 2045. This study presents the first estimations of the number of crashes that could be reduced using LDP and therefore could have significant impacts on public and industry adoption rates of the technology. The results of this study influence policy making and regulatory approaches to improving motor vehicle safety and further recommend education and outreach activities to increase awareness on the benefits of LDP technology. • This study forecasts how gradual adoption of vehicles with lane departure prevention (LDP) technology can reduce crashes. • By 2020, 2.7% of single-vehicle lane departure crashes could be avoided if 8.5% of the fleet has LDP with 20% effectiveness. • With increase in the effectiveness of LDP technology, the reduction in relevant crashes increased. • If the LDP system is 100% effective by 2045, 66.5% of the single-vehicle lane departure crashes can be prevented. [ABSTRACT FROM AUTHOR]

The media continues to be a prominent and widely embraced channel for obtaining information on various topics, exerting considerable influence in shaping public opinions. This holds particularly true for the realm of automated vehicles (AVs), as the media consistently covers developments in this field, encompassing both positive aspects such as technological advancements, as well as negative occurrences like incidents involving self-driving cars colliding with pedestrians. The objective of this study was to utilize sentiment analysis techniques to evaluate the portrayal of AV technology in media news coverage. Data was retrieved from Sprinklr using keywords that are often associated with AVs. Over 1.7 million articles were collected on the keywords for relevant articles published between May 1, 2015 to May 24, 2021. Sentiment analysis was carried out on the cleaned data using three different models – VADER, TextBlob, and NRCLex. The sentiment analysis was separately conducted on the title and text of the articles. 2018 recorded the highest number of news articles on the AV technology. The number of negative sentiments in the title of articles published on the web in 2018 increased 12 times compared to the previous year. The negativity induced in 2018 in the news media did not continue in the next year, which explains that the technology experienced short term effects from the negative incidents associated with its early development. The news articles were also found to have subjectivity from the authors in the article text. The findings are expected to stimulate debates among industry players on how to bring the media along the journey of the development and eventual full deployment of AVs. • The negative sentiment is short lived in the news media articles. • Positivity and trust were the strongest sentiments in the media articles text. • Authors of the news media articles incorporated their subjectivity into the text. [ABSTRACT FROM AUTHOR]