Introduction Of Autonomous Vehicles Research Articles

Study of Human Comfort in Autonomous Vehicles Using Wearable Sensors

The rapid development of autonomous vehicles (AVs) has depicted a promising future of a safer and more efficient transportation system. To better induce this revolution, massive efforts have been spent on the technical competence of AVs. However, the human comfort in AVs has been an under-discussed yet important topic to the user acceptance of AVs. For the detection of human comfort, existing studies focus more on the physical influential factors of comfort such as sitting posture, vibration, and noise. With the introduction of AVs, psychological factors also have gained greater influence on human comfort. Despite existing studies of exploring correlations between human comfort and some physiological signals in automated driving contexts, there is few study on how human comfort level in AVs can be detected with these physiological signals. In this paper, we developed effective human comfort study approaches in autonomous vehicles with wearable sensors. We also proposed a machine learning based approach with adaptive feature selection to detect human comfort levels based on the wearable sensing data. The experimental results illustrated the effectiveness of the proposed approaches in studying human comfort in AVs.

Stakeholder viewpoints analysis of the autonomous vehicle industry by using multi-actors multi-criteria analysis

The availability of autonomous vehicles (AVs) on the market will create a novel situation for every stakeholder. A lot of research has been conducted on developing the AVs and the consequences of having AVs on the market, but comprehensive studies concerning various viewpoints on the introduction of AVs into the market can be scarcely found. This research is examining different actors' viewpoints on the acceptance of privately shared autonomous vehicles (PSAVs). Four groups of stakeholders are identified as the following: users, legislators, operators, and manufacturers. The multi-actor multi-criteria analysis (MAMCA) is used, where the analytical hierarchy process (AHP) and the parsimonious AHP (PAHP)methods are applied to evaluate each actor's objectives and criteria. From each group, a representative sample is collected, the actors evaluate a set of pairwise comparison matrices (PCMs), and their consistency is continuously checked. As a result, the objectives and the criteria are ranked and presented. The main finding of the objectives' analysis presents that the safety concerns receive the highest ranking (i.e., weight is 0.085), while the ease of use and the interoperability across the borders have the lowest rankings ((i.e., weight is 0.048, and 0.0515, respectively). The main finding of criteria's analysis shows that ease of use has the lowest ranking (i.e., weight is 0.0024), and the highest rank is related to the reduction in vehicle accidents caused by the malfunction (i.e., weight is 0.0495). Thus, it seems that safety related issues are the most important factor in accepting PSAV. The result of this study is useful for decision-makers and transport planners to form policies, regulations, and guidelines regarding the future implementations of PSAVs before their arrival to the market.

Assessing the Impacts of Autonomous Vehicles on Road Congestion Using Microsimulation.

The introduction of autonomous vehicles has been considered as a possible option for reducing traffic congestion in many transport studies. Many types of models, in particular car-following microsimulation models have been adopted in most studies. The impacts of autonomous vehicles (AVs) on congestion, however, have not yet been concluded. This could be because different researchers use different forms of car-following models to assess these impacts, or because the utilised modelling approaches and their parameters are different in different studies. In particular, two of the important parameters that are associated with car-following models are the used values for maximum acceleration and the average desired time gaps. While the values of these parameters can be adjusted and controlled by the ACC controllers in the AV, they can also be controlled by the users. Therefore, assigning unrealistic values to these parameters could well result in unrealistic conclusions. This paper investigated the impacts of the maximum acceleration and the average desired time gaps on congestion levels using the loss-time indicator. The analysis was carried out on the Hanshin expressway in Japan and was tested and assessed using the Helly (FACC) car-following microsimulation model. This includes estimating the values of the desired time gap from real traffic time-gap distributions. The Hanshin expressway is an urban toll highway of 273 km that extends from Osaka to Kobe, representing the Hanshin area in Japan. The Hanshin highway serves a huge traffic volume that consists of private and freight vehicles that operate within the Hanshin area. This area represents one of three major municipal areas in Japan including Tokyo and Nagoya. A total of 740,000 vehicles per day travel on the expressway. As a result, there is significant congestion on the Hanshin expressway. There have been various plans put in place to ease congestion ranging from building new roads to the implementation of traffic-demand-management measures. However, the predictions of the impacts of such measures do not provide any evidence that they would ease traffic congestion. Other possible measures that could be investigated for easing traffic congestion include technology-based solutions such as autonomous vehicles. The modelling results recommend that the results obtained from microsimulation models should be taken with care, and good attention should be paid to the parameters used and their values in the model. The values assigned to driving-behaviour parameters, the maximum values of acceleration, and the time-gap settings, for example, control the final outcomes of the models.

Beyond the Steering Wheel: Exploring the Frontiers of Automotive Technology

This research explores the transformative impact of emerging automotive technologies on safety, sustainability, and the future of transportation. Leveraging empirical data, including accident analysis, consumer surveys, and qualitative research, the study investigates the adoption and implications of autonomous vehicles, the shifting consumer preferences towards electric propulsion, and the evolving expectations regarding vehicle connectivity. The findings reveal a significant correlation between the adoption of autonomous vehicles and a substantial reduction in traffic accidents. The study reports a 23% decrease in accidents following the introduction of autonomous vehicles, emphasizing their potential to enhance road safety and reduce associated healthcare costs. Consumer preferences are shifting towards electric vehicles (EVs), with 68% of surveyed vehicle owners expressing interest in EVs as their next car purchase. Environmental concerns (43%) and cost savings (32%) are the primary motivators, highlighting the need for increased investment in EV infrastructure and incentives to support sustainable transportation. Qualitative data analysis identifies connectivity features as pivotal in redefining the driving experience. Enhanced infotainment systems, advanced driver-assistance features, and seamless smartphone integration are among the key features driving consumer expectations. The study underscores the importance of integrating advanced technology into future vehicle models to meet evolving consumer demands. The implications of these findings are significant for various stakeholders, including policymakers, automotive manufacturers, and consumers. The study recommends continued investment in autonomous vehicle technology, expansion of EV infrastructure, innovation in connectivity features, consumer education, and industry collaboration to shape a safer, more sustainable, and technologically advanced future of transportation.

EVALUATION OF THE IMPACTS OF AUTONOMOUS VEHICLES ON THE MOBILITY OF USER GROUPS BY USING AGENT-BASED SIMULATION

An agent-based transport simulation model is used to examine the impacts of Autonomous Vehicles (AVs) on the mobility of certain groups of people. In the state of the art, it has been found that the researchers primarily have simulation studies focusing on the impacts of AVs on people regardless of certain groups. However, this study focuses on assessing the impacts of AVs on different groups of users, where each group is affected variously by the introduction of different penetration levels of AVs into the market. The Multi-Agent Transport Simulation (MATSim) software, which applies the co-evolutionary algorithm and provides a framework to carry out large-scale agent-based transport simulations, is used as a tool for conducting the simulations. In addition to the simulation of all travellers, 3 groups of users are selected as potential users of AVs, as follow: (1) long commuters with high-income, (2) elderly people who are retired, and (3) part-time workers. Budapest (Hungary) is examined in a case study, where the daily activity plans of the households are provided. Initially, the existing daily activity plans (i.e., the existing condition) of each group are simulated and assessed before the introduction of AVs into the market. After that, the AVs are inserted into the road network, where different fleet sizes of AVs are applied based on the demand of each group. The marginal utility of the travel time spent in case of a transport mode, the AV fleet size, and the cost of the travel are the key variables that determine the use of a transport mode. The key variables are set based on the characteristics of the case study (i.e., demand) and the AVs. The results of the simulations suggest that the AVs have different degrees of influences on certain groups as demonstrated in the occurred changes on the modal share. The value of changes depends on the Value of Travel Time (VOT) of people and the used fleet size of AVs. Moreover, the influence of the traveller’s characteristics on the AVs is manifested, such as different values of fleet utilization. Furthermore, the study demonstrates that an increase in the fleet size of AVs beyond 10% of the demand does not significantly raise the modal share of AVs. The outcome of this paper might be used by decision-makers to define the shape of the AVs’ use and those groups who are interested in using AVs.

Performance Evaluation of VANET Routing Protocols in Madinah City

Traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and Vehicular ad hoc networks (VANETs) to assist in communication and alleviation of traffic congestions. The primary goal of this study is to evaluate the performance of communication routing protocols in VANETs between autonomous and human-driven vehicles in Madinah city in varying traffic conditions. A simulation of assorted traffic distributions and densities were modeled in an extracted map of Madinah city and then tested in two application scenarios with three ad hoc routing protocols using a combination of traffic and network simulation tools working in tandem. The results measured for the average trip time show that opting for a fully autonomous vehicle scenario reduces the trip time of vehicles by approximately 7.1% in high traffic densities and that the reactive ad hoc routing protocols induce the least delay for network packets to reach neighboring VANET vehicles. From these observations, it can be asserted that autonomous vehicles provide a significant reduction in travel time and that either of the two reactive ad hoc routing protocols could be implemented for the VANET implementation in Madinah city. Furthermore, we perform an ANOVA test to examine the effects of the factors that are considered in our study on the variation of the results.

A Hybrid Approach Toward Efficient and Accurate Intrusion Detection for In-Vehicle Networks

With recent advancements in the automotive world and the introduction of autonomous vehicles, automotive security has become a real and important issue. Modern vehicles have tens of Electronic Control Units (ECUs) connected to in-vehicle networks. As a de facto standard for in-vehicle network communication, the Controller Area Network (CAN) has become a target of cyber attacks. Anomaly-based Intrusion Detection System (IDS) is considered as an effective approach to secure CAN and detect malicious attacks. Currently, there are two primary approaches used for intrusion detection: rule-based and machine learning-based. Rule-based approach is efficient but limited in the detection accuracy while machine learning-based detection has comparably higher detection accuracy but higher computation cost at the same time. In this paper, we propose a novel hybrid IDS that combines the benefits of both rule-based and machine learning-based approaches. More specifically, we use machine learning methods to achieve a high detection rate while keeping the low computational requirement by offsetting the detection with a rule-based component. Our experiments with CAN traces collected from four different vehicle models demonstrate the effectiveness and efficiency of the proposed hybrid IDS.

Traffic Safety Under Mixed Traffic Flow: An Approach Towards Mix Modal Transport Planning

Mixed Traffic flow is an important component in consideration in present day Transport Planning in the current scenario and future of transport planning in India. As country is welcoming the accent of Advanced technology, an introduction of Autonomous vehicles, a need of study raised for the safe flow of traffic into the mixed condition. This paper aims to study the traffic condition of current times comparing with the previous traffic records, to establish the context of mixed use and the favourable condition or strategy to maintain a safe flow of traffic under mixed flow condition. Traffic meagre is studied to understand the mix of AT and NA, which is a major concern to be studied. Safety features are intended to prevent accidents and collisions by providing technologies that notify the driver to potential hazards, execute protections, and, if required, take control of the vehicle. These controls are to be implemented on both the Manual as well as Autos traffic. For an efficient usage of the same the major need arises for the implementation of Intelligent traffic management system through technologically advanced equipment such as sensors, camera and data management. Some determinants are identified to locate the major key issues that indicate towards the traffic safety and further analysis is done towards the implementation goals for through and merging traffic including both autos and non-autos

The Legal Issues in the Introduction of Autonomous Vehicles in India

The present article shall attempt to discuss the nuances of autonomous technology and its direct correlation with the existing legal infrastructure in India as well as the future prospects of the same. Before the legal issues are addressed in this paper, it is incumbent to briefly establish the premise, the purpose and the technology involved in the development of artificial intelligence operated vehicles. The communication system and its interaction with the other components of the vehicle are also addressed. The legal issues considered here are limited to the admissibility of autonomous vehicles under the Motor Vehicles Act, 1988, the concerns of erosion of data privacy, the moral dilemma and decision-making ability in moral situations and the nascent context of cyber-hacking. Their imminent introduction in the near future requires an immediate stabilization of legal principles and provisions to suit vulnerabilities and strengths of such an advanced technology and the shortcomings of the present legislation highlighted.

Self-protective and self-sacrificing preferences of pedestrians and passengers in moral dilemmas involving autonomous vehicles.

Upon the introduction of autonomous vehicles into daily traffic, it becomes increasingly likely that autonomous vehicles become involved in accident scenarios in which decisions have to be made about how to distribute harm among involved parties. In four experiments, participants made moral decisions from the perspective of a passenger, a pedestrian, or an observer. The results show that the preferred action of an autonomous vehicle strongly depends on perspective. Participants’ judgments reflect self-protective tendencies even when utilitarian motives clearly favor one of the available options. However, with an increasing number of lives at stake, utilitarian preferences increased. In a fifth experiment, we tested whether these results were tainted by social desirability but this was not the case. Overall, the results confirm that strong differences exist among passengers, pedestrians, and observers about the preferred course of action in critical incidents. It is therefore important that the actions of autonomous vehicles are not only oriented towards the needs of their passengers, but also take the interests of other road users into account. Even though utilitarian motives cannot fully reconcile the conflicting interests of passengers and pedestrians, there seem to be some moral preferences that a majority of the participants agree upon regardless of their perspective, including the utilitarian preference to save several other lives over one’s own.

Incentivizing Efficient Equilibria in Traffic Networks With Mixed Autonomy

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: 1) human drivers, who will choose the quickest route available to them, and 2) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks that, we show, can be efficiently calculated.

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

Autonomous driving is a technological innovation that involves the use of Artificial Intelligence (AI) in the automotive area, representing the future of transport and whose applications will influence the concept of driving and many other features of modern society. Indeed, the introduction of Autonomous Vehicles (AVs) on the market, along with the development of related technologies, will have a potential impact not only on the automotive industry but also on urban transport systems. New mobility-related businesses will emerge, whereas existing ones will have to adapt to changes. There are various aspects that affect urban transport systems: in this work, we highlight how road markings, intersections, and pavement design upgradings have a key role for AVs operation. This work aims to describe how contemporary society will be influenced by Autonomous Vehicles’ spread in regard to urban transport systems. A comprehensive analysis of the expected developments within urban transport systems is hereby presented, and some crucial issues concerning benefits and drawbacks are also discussed. From our studies, it emerges that the detection performed by vehicles is mainly affected by road markings characteristics, especially at intersections. Indeed, the need for a new cross-sections type arise, since vehicles wandering phenomena will be reduced due to AVs position-keeping systems.

The importance of equation η = μn 2 in dimensional analysis and scaled vehicle experiments in vehicle dynamics

Dimensional analysis has been very helpful in experimentation of very large or small-scale engineering systems. A good example would be experimentation on the aircrafts and ships, which was made cost-effective and simple by dimensional analysis. The history of dimensional analysis mentioned in the introduction section of the present document includes many of such applications. Automotive industry, however, never felt the need as the price or the size of land vehicles did not make experimentation so far-fetched; therefore, there are many crash tests which every new vehicle has to go through before mass production This changed with the imminent introduction of autonomous vehicles, which brought all the risks involved in experimenting with them. Many cost-effective experimental platforms are introduced, such as QCar https://www.quanser.com/products/qcar/ or laboratories, such as Scaled Autonomous Vehicles Indoor (SAVI) https://cast.tamu.edu/research/technology-demonstrator-platforms/scaled-autonomous-vehicles-indoor-tdp/. The present study will enable the results taken from such platforms to be translated to real-sized vehicles, enabling researchers to study dynamics of various vehicles. Classical vehicle equations of motion including constant velocity, accelerating bicycle model and roll model have been made dimensionless. The case of steady-state responses is also calculated in a dimensionless form. Some practical numerical examples are also mentioned as a proof of theory.

“They have to be better than human drivers!” Motorcyclists’ and cyclists’ perceptions of autonomous vehicles

Road users and the general population by and large recognise the value of vehicles with automated driving systems and features (otherwise typically known as Autonomous Vehicles (AVs)) in terms of road safety, reduced emissions and convenience, but are still wary of their capability, preferring the ‘comfort zone’ of human operator intervention. Motorcyclists and cyclists conversely, are vulnerable to human fallibility in driving, with the majority of crashes occurring as a consequence of other drivers’ inattention. The transition period associated with the introduction of AVs will require AVs and motorcyclists/cyclists sharing the road for a number of years yet, so we need to understand motorcyclists’/cyclists’ perception of AVs. The question of interest here is whether motorcyclists/cyclists reflect the historical literature in this area by having higher levels of trust for human drivers over AVs, or whether they have higher levels of trust in AVs because it removes the ‘human element’ that has been proven to be particularly dangerous for them. Here we surveyed motorcyclists and cyclists about their trust in human drivers and AVs, and developed a novel suite of questions designed to interrogate the difference between trust in general versus trust as a concept of their own personal safety. Some of the salient outcomes suggest that motorcyclists have medium to low levels of trust for both human drivers and AVs, but are significantly more likely to believe that AVs are safer in terms of their own personal safety, such as prioritising or detecting the rider, compared to human drivers. This relationship varies with age and crash experience. The results here are consistent with the logic that motorcyclists/cyclists have a heightened sense of vulnerability on the road and welcome the introduction of AVs as a way of mitigating personal risk when riding. This insight will be crucial to the subsequent roll-out of AVs in the future.

Cross-National Focus Group Response to Autonomous Vehicles

The development of autonomous vehicles (AVs) is advancing quickly in some enclaves around the world. Consequently, AVs exist in the public consciousness, featuring regularly in mainstream media. As the form and function of AVs emerge, the attitudes of potential users become more important. The extent to which the public trusts AV technology and anticipates benefits, will drive consumer willingness to use AVs. Broadly, public attitudes will determine whether AVs can attract public investment in infrastructure and become a feature of the future transport mix or fail to realize the potential their developers assert. As part of UK Autodrive, a program trialing the introduction of AVs in the United Kingdom, researchers conducted focus groups in five UK cities, and a comparison focus group in San Francisco (December 2017 to September 2018) using representative samples (total n = 137). Focus group facilitators guided discussions in three areas considered central to usage decisions: trust in the technology, ownership models, and community benefit. This paper describes findings from a quasi-quantitative study supported with qualitative insights. This research provides three key takeaways centering on trust in the technology and in delivering benefit. First, some participants gain trust through experience and others through evidence. Second, participants had difficulty discriminating between AV developers, indicating a need for industry cooperation. Third, partnerships were found to demonstrate trust, highlighting the need for more and deeper partnerships moving forward. Generally, participants had positive attitudes toward AVs and expect AVs to provide benefits. However, these attitudes and expectations could change as AV development progresses.

Quantitative study of vehicle-pedestrian interactions: Towards pedestrian-adapted lighting communication functions for autonomous vehicles

This paper reports the main conclusions of a fielding observation of vehicle-pedestrian interactions at urban crosswalks, by describing the types, sequences, spatial distributions and probabilities of occurrence of the vehicle and pedestrian behaviors. This study was motivated by the fact that in a near future, with the introduction of autonomous vehicles (AVs), human drivers will become mere passengers, no longer being able to participate into the traffic interactions. With the purpose of recreating the necessary interactions, there is a strong need of new communication abilities for AVs to express their status and intentions, especially to pedestrians who constitute the most vulnerable road users. As pedestrians highly rely on the actual behavioral mechanism to interact with vehicles, it looks preferable to take into account this mechanism in the design of new communication functions. In this study, through more than one hundred of video-recorded vehicle-pedestrian interaction scenes at urban crosswalks, eight scenarios were classified with respect to the different behavioral sequences. Based on the measured position of pedestrians relative to the vehicle at the time of the significant behaviors, quantitative analysis shows that distinct patterns exist for the pedestrian gaze behavior and the vehicle slowing down behavior as a function of Vehicle-to-Pedestrian (V2P) distance and angle.

Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications

In upcoming years, the introduction of autonomous vehicles (AVs) will reshape the transport system. The transition from a regular to an autonomous transport system, however, will take place over many years and lead to a long period with a mixed driving environment where AVs and regular vehicles (RVs) operate side by side. The purpose of this study is to investigate how the utilisation of the road capacity degrades as a function of heterogeneity in congested motorways. The analysis is based on a dedicated traffic simulator, which enables the investigation of complex dynamic spillback from congestion while allowing for different degrees of heterogeneity. The representation of autonomous vehicles is based on a modified intelligent driver model (IIDM) presented by Treiber et al. (Phys Rev E 62(2):1805–1824, 2000) and Treiber and Kesting (Traffic flow dynamics, Springer, Heidelberg, 2013), while the behaviour of drivers of RVs relies on a stochastic version of the IIDM. Three main conclusions stand out. Firstly, it is shown that in an idealised environment in which AVs operate alone, a substantially improved capacity utilisation can be attained. Secondly, when drivers of RVs are mixed with AVs, capacity utilisation degrades very fast as a function of the share of RVs. Thirdly, it is shown that the improved capacity utilisation of AVs comes in the form of reduced travel time and increased throughput, with indications that travel time reductions are the most important. From a strategical planning perspective, the results underline that dedicated lanes are preferable to attain the positive effects of AVs. Specifically, we compare a stylised situation with three lanes with a share of 33% AVs to a situation with two regular lanes and a single dedicated AV lane. The latter represents a tripling in consumer surplus all other things being equal.

Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated driving vulnerable to machine learning-based attacks. In this work, we explore the backdooring/trojanning of DRL-based AV controllers. We develop a trigger design methodology that is based on well-established principles of traffic physics. The malicious actions include vehicle deceleration and acceleration to cause stop-and-go traffic waves to emerge (congestion attacks) or AV acceleration resulting in the AV crashing into the vehicle in front (insurance attack). We test our attack on single-lane and two-lane circuits. Our experimental results show that the backdoored model does not compromise normal operation performance, with the maximum decrease in cumulative rewards being 1%. Still, it can be maliciously activated to cause a crash or congestion when the corresponding triggers appear.

Autonomous Vehicle Control in CARLA Challenge

The introduction of Autonomous Vehicles (AVs) in a realistic urban environment is an ambitious objective. AV validation on real scenarios involving actual objects such as cars or pedestrians in a wide range of traffic cases would escalate the cost and could generate hazardous situations. Consequently, autonomous driving simulators are quickly evolving to cover the gap to achieve a fully autonomous driving architecture validation. Most used 3D simulators in self-driving cars field are V-REP (Rohmer, E., 2013) and Gazebo (Koenig, N. and Howard, A., 2004), due to an easy integration with ROS (Quigley, 2009) platform to increase the interoperability with other systems. Those simulators provide accurate motion information (more appropriate for easier scenes like robotic arms) but not a realistic appearance and not allowing real-time systems, not being able to recreate complex traffic scenes. CARLA (Dosovitskiy, A., 2017) open-source AV simulator is designed to be able to train and validate control and perception algorithms in complex traffic scenarios with hyper-realistic environments. CARLA simulator allows to easily modify on-board sensors such as cameras or LiDAR, weather conditions and also the traffic scene to perform specific traffic cases. In Summer 2019, CARLA launched its driving challenge to allow everyone to test their own control techniques under the same traffic scenarios, scoring its performance regarding traffic rules. In this paper, the Robesafe researching group approach will be explained, detailing vehicle motion control and object detection adapted from Smart Elderly Car (Gómez-Huélamo, C., 2019) that lead the group to reach the 4th place in Track 3 challenge, where HD Map, Waypoints and environmental sensors data (LiDAR, RGB cameras and GPS) were provided.

Sharing the road with autonomous vehicles: Perceived safety and regulatory preferences

Technology providers, car manufacturers, and public agencies all need to work together to undertake extensive testing of fully autonomous vehicles (AVs) on public roads before such AVs are allowed to freely travel in ways similar to human-driven vehicles. This raises the importance of understanding public perceptions regarding safety considerations when traveling alongside AVs. This study makes use of a national survey conducted by the Pew Research Center to identify the affective, socio-demographic and technology-use attributes that affect an individual’s perception of the safety of sharing the road with AVs (PSSRAV) and identifies measures and interventions that can be undertaken to improve PSSRAV. Additionally, we evaluate individual preferences for AV regulations. Our results underscore the importance of the need for service providers and public agencies to be cognizant of the demographic and lifestyle/affective emotion considerations shaping AV safety perceptions and opinions about AV regulations. In particular, there is a need not only to focus on technological and other infrastructure components of AV development, but also to recognize the socio-technical considerations and human-related factors of the end-users. Our findings should be of substantial interest in the planning, design, deployment, and introduction of AVs within a safe and minimally regulated public operating arena.