Evolution towards optimal driving strategies for large‐scale autonomous vehicles

Multi-objective optimization problems (MOP) are frequently encountered in practice. In some cases, different computationally expensive analyses may be independently used for computing different objectives of the MOP. Additionally, the analyses may be executed to obtain estimates with different fidelity, typically higher fidelity requiring a longer run-time. For instance, in automotive design, the aerodynamic drag is computed using computational fluid dynamic (CFD) analysis and its crashworthiness/strength is assessed using finite element analysis (FEA). Both the objectives can be independently computed and the underlying fidelity of each analysis can also be controlled using different mesh sizes/thresholds on the residual errors. While there exist a number of generic MOP benchmark problems in the literature, there is scarce work on constructing MOPs with multi-fidelity (MF) analyses to support the development of multi-fidelity, multi-objective optimization algorithms. The existing MF benchmarks are limited to unconstrained, single-objective optimization problems only. Towards addressing this gap, in this paper, we introduce a test suite for multi-objective, multi-fidelity optimization (MOMF). The problems are derived by combining existing unconstrained, multi-objective design optimization problems with resolution/stochastic/instability errors that are common manifestations of MF simulations. The method allows for the construction of any number of low-fidelity functions with desired level of correlations for a given high-fidelity objective function. We hope that the test suite would motivate novel algorithmic developments to support optimization involving computationally expensive and independently evaluable objectives.

Connected vehicle (CV) and autonomous vehicle (AV) technologies are rapidly advancing. Traffic control for future hurricane evacuation can be radically different compared to the current approaches in use. Extensive modeling research and limited field deployments have demonstrated the benefits of CV, AV technologies in achieving higher mobility, safety, and environmentally friendly solutions. One area that has not been explored in prior research is the benefits these technologies may offer to the emergency evacuation performance. Specifically, evacuation traffic control is one area where CV and AV technologies can have a transformative effect. At-grade intersections and grade-separated interchanges on evacuation routes are susceptible to congestion due to the increased demand competing for road usage. Traffic control methods that efficiently route traffic at intersections and interchanges also enhance the overall evacuation performance. This paper proposes an autonomous reservation-based intersection control (AReBIC) system for evacuation traffic control. The AReBIC system receives and processes reservation requests from approaching vehicles in real-time and routes them free of conflict. The results of a case study of Hampton Roads, Virginia, showed that the AReBIC system outperformed the existing traffic control practice in terms of mobility and safety. The average speeds increased by 29% and total delays reduced by 88% when intersection control was changed to the AReBIC system. The number of crossing conflicts decreased by 88% and lane change conflicts decreased by 47%.

Although various optimization methods for solving single-objective problems have been developed in the last few decades, these methods have lost their eligibility due to the fact that today’s engineering problems are toward multiple objective optimization problems, in real applications. For single-objective optimization problems, for example, in case of a minimization problem, this value is the decision vector giving the smallest objective that can be achieved within the specified constraints. Hence the minimum decision vector within all possible (feasible) solution vectors is the so-called optimal solution and/or optimal design. However, in multi-objective optimization problems, since a different objective value is generated against each decision vector, the superiority of the solutions over each other is determined by considering the trade-off among the objective values. Therefore, the solution of multi-objective optimization problems, unlike single-objective problems, is a set of vectors rather than a single decision vector. In multi-objective optimization problems, especially if there are intricate objectives, the computational cost of the problem increases. In other words, while synchronously trying to maximize one of the objectives and to minimize another one makes it difficult to find the global optimum design. One of the important techniques used in multi-objective optimization problems is Pareto optimality which enables to select the global optimum solution taking into account the trade-off among all objectives. In this context, using of derivative-based methods has decreased, but the use of metaheuristic methods has increased due to the rapid availability of global optimum solution. This is because the improvements in the field of optimization are progressing in proportion to technology and varying according to the needs. In this chapter, one of the recent metaheuristic optimization methods based on swarm intelligence that is so-called a Pareto-based bat algorithm inspired by the behavior of determining the direction and distance of an object using the echo of the sound called the echolocation of bats is used in order to obtain optimum solutions for multi-objective engineering design problems. In this regard, a four-bar planar truss, a real-sized welded steel beam as well as a multi-layer radar absorber are selected as multi-objective engineering design optimization problems. In case the obtained results (optimal designs) are examined, the potency and the reliability of the proposed multi-objective Pareto-based bat algorithm are demonstrated.

As autonomous vehicle (AV) technology progresses, a substantial amount of AV simulation work has studied potential future outcomes that could occur given different assumptions, scopes and technologies. As a revolutionary technology, AVs could have significant impacts on urban economic, environmental and social systems. In many cases, the research is directed towards decisionmakers: transportation authorities, policymakers and fleet operators. However, due to the complexity and uncertainty of AV adoption in interconnected urban systems such as transportation and land use, it is difficult for policymakers to understand the overlapping policy suggestions and results across the many simulation studies. In this paper, we summarise the suggested policies in AV simulation research from 170 studies published during 2016–2024 and provide context for the policies being suggested. Specifically, the key question we try to answer is: what are the recommended AV policies based on simulation studies? Many simulation results have shown increased vehicle miles/kilometers travelled (VMT/VKT) due to induced demand, empty vehicle repositioning, or cruising instead of parking. In response, we found common policy recommendations such as congestion pricing, subsidising car-sharing and ride-pooling, increasing parking costs, investing in public transportation and restricting AV demand as ways to address the increase in VMT/VKT. We also identified common policy bundles, or joint recommendations, such as congestion pricing with increased parking costs. Finally, we compare and contrast simulation policy suggestions with a few qualitative AV policy studies, pointing out key research gaps for the future and possible mixed-methods opportunities.

Nowadays, scientific developments in the vehicle and infrastructure automation sectors are developing steadily to benefit end users in ways like reducing traffic congestion, enhancing safety, facilitating stress-free travel, reducing fuel costs, and enabling smart parking. Autonomous vehicles sometimes known as self-driving cars, can entirely replace conventional automobiles to accomplish safe and reliable intelligent transportation systems (ITS). Autonomous vehicles have improved sensing capabilities and can drive on roads that would be difficult for humans. Software, technology, and people, together with their interactions, are the building blocks of Autonomous vehicles. Even though Autonomous vehicles have been studied extensively, many issues have yet to be fixed. One of the biggest obstacles to developing Autonomous vehicles is improving their ability to communicate with other vehicles and pedestrians. Second, it's unclear whether society will benefit or suffer from people's tendency to place an excessive amount of faith in Autonomous vehicles and assume that they will never malfunction. Capabilities analysis is essential to making sure autonomous vehicles are safe and widely accepted. The proposed review outlines the key benefits, drawbacks, and difficulties of autonomous vehicle technology and provides a detailed analysis of the different autonomous vehicle mechanisms. Further, the developed review intends to formulate future prospects by examining the advantages and disadvantages of autonomous driving technology to encourage the development of innovative autonomous vehicle technologies to prevent human error and increase road safety and security.

Autonomous ground vehicles have evolved a lot in the past decade. Several prototypes have been presented and tested in different conditions. A major challenge for such vehicles is the fact that they depend on advanced sensing technologies to perceive the environment and make real-time decisions. The complexity of this task typically requires multiple sensing devices, which is both a technical challenge as well as a limiting factor for autonomous vehicles evolution to mass-production. In this paper we review the current main sensing technologies for autonomous ground vehicles, their suitability and maturity status, as well as the way they have been used in prototype cars. This analysis gives a perspective of their evolution so far, as well as an indication of possible future trends.

Human acceptance of autonomous vehicles: Research status and prospects

Preparing drivers for the future: Evaluating the effects of training on drivers’ performance in an autonomous vehicle landscape

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

Technology of autonomous vehicles (AVs) is getting mature and many AVs will\nappear on the roads in the near future. AVs become connected with the support\nof various vehicular communication technologies and they possess high degree of\ncontrol to respond to instantaneous situations cooperatively with high\nefficiency and flexibility. In this paper, we propose a new public\ntransportation system based on AVs. It manages a fleet of AVs to accommodate\ntransportation requests, offering point-to-point services with ride sharing. We\nfocus on the two major problems of the system: scheduling and admission\ncontrol. The former is to configure the most economical schedules and routes\nfor the AVs to satisfy the admissible requests while the latter is to determine\nthe set of admissible requests among all requests to produce maximum profit.\nThe scheduling problem is formulated as a mixed-integer linear program and the\nadmission control problem is cast as a bilevel optimization, which embeds the\nscheduling problem as the major constraint. By utilizing the analytical\nproperties of the problem, we develop an effective genetic-algorithm-based\nmethod to tackle the admission control problem. We validate the performance of\nthe algorithm with real-world transportation service data.\n

The present work focuses on obtaining optimal operational policies of an industrial scale penicillin fed-batch fermentation process using dynamic optimization (DO). The three process objectives considered in this work include, maximization of total penicillin formed (J 1), minimization of total biomass formed (J 2), and minimization of fed-batch operation time (J 3). The control variables for the optimization study include, the feed flowrates of sugar, soyabean oil, and phenylacetic. We perform a single-objective optimization study with J 1 as the objective function and showcased the importance of DO. Consequently, we solve three multi-objective optimization (MOO) problems corresponding to all the pairwise combinations of the aforementioned three objectives. Outcomes of MOO problems are presented in terms of several non-dominated Pareto-optimal solutions. Furthermore, descriptive analysis of three representative Pareto points for all the MOO problems is presented, which highlights the importance of MOO study of the penicillin bio-reactor.

Problem definition: The ongoing large-scale deployment of autonomous vehicle (AV) technology has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. Our work studies the incentives of stakeholders in such deployments, possible misalignments, and contracting solutions to overcome them. Methodology/results: We consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human individual contractors (ICs), including (1) an open platform in which the AV supplier and ICs bring their own vehicles to the system, (2) an AV-supplier operated platform without ICs, (3) a platform that sources AVs through leasing contracts, and (4) an integrated supply chain. We find that except for the AV-only platform, all deployment models are subject to a risk of AV underutilization due to the need to maintain the ICs’ utilization sufficiently high to ensure ICs remain engaged. As AV underutilization propagates in a nonintegrated supply chain, an open platform can become arbitrarily worse than supply chain integration, and careful usage commitments are needed to overcome the efficiency loss. Managerial implications: This paper identifies a new kind of supply chain misalignment that is likely to emerge as AVs become deployed technology, which both platform operators and AV suppliers should be mindful of. Moreover, we demonstrate the value of usage commitment in the deployment of AVs through an open platform, which makes it more efficient than AV-only/AV leasing platforms. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This particular paper was part of the 2024 MSOM Service Operations SIG Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1175 .

Different perspectives discuss autonomous vehicle (AV) as predicted future transportation that includes design, engineering, economics, computer science, social, economic, legislation, and ethical aspects. On the other hand, AV technology offers new problems such as new engineering aspects, social and ethical problems, and presents in the form of an idealized decision-making problem. There are many benefits to AV vehicles, which are at the forefront of technology development. However, the level of cybersecurity provided by this technology is poorly understood. Cyber-attacks against AV can have severe implications for safety, privacy, manufacturers' reputation, and overall perception. In this research, we examine public opinion regarding the implications and challenges that might face AV technology in the United Arab Emirates (UAE). We claim that the successful adoption of AV in future transport systems will be achieved by reasonable risk management approaches that deal with different potential threats of cybersecurity. This survey produced usable responses from 204 participants. The significant result was that the respondents had not previously heard of vehicle-related technology; however, most had a positive first opinion on technology. Around 85% of participants felt that the expected benefits were likely to occur. They expressed a high level of concern regarding security and performance issues. Respondents stated that safety is the most crucial aspect of driverless vehicles. Mostly, it stated that personal communication devices should integrate with the AV's internet connection. The primary outcomes are that the population feels positive about AV and have optimistic expectations regarding its benefits. While maintaining some concerns, most of the people are willing to drive the AV.

Autonomous vehicles are the group of vehicles that do not or partially need a human driver to drive them. So, they are also known as driverless or robotic vehicles. Despite the name or group or size of vehicles, the purpose behind the technology is same. Besides the rapid development in technology of autonomous vehicles, the experiments have been started in early 1920 and due to lack of highly powerful computing devices, they were just controlled by radio wave technologies popularly known as remotes. Then, later on, the research and development took its speed in 1950 with the development of computers. But, nowadays with the development of highly powerful and cost-efficient computing hardware GPU the enhancement in technology becomes so rapid that it is updating day by day and becoming capable of concurring every aspect of human drivers’ skills. AI and Machine Learning algorithms are being developed to train the model for autonomous driving over huge amount of recorded driving data of human drivers. As the time goes on these models started mimicking drivers’ behavior and improves it day by day. So, as these Autonomous driving getting normalize and passing various parameters for driving the automobile industries saw a large potential for business and profit in autonomous vehicles leading the giant companies (Waymo Google, Tesla, Volvo, Renault, Uber, Toyota, Audi, Mercedes-Benz, Nissan, General Motors, Bosch and Continental's motors) started developing Level-3 Autonomous vehicles and released in 2020. By the rigorous development and solving challenges, autonomous vehicles are becoming our irreplaceable need for future.KeywordsAutonomous vehiclesArtificial intelligenceMachine learning

Autonomous vehicle (AV) technologies are expected to play a crucial role in reducing traffic crashes occurred by human factors; however, foreseeable coexistence of AVs with human-driven vehicles (HDVs) for an extended transition period raises safety concerns. Understanding factors influencing AV-involved crashes is crucial, especially as human drivers may struggle to comprehend the behavior of AVs during interactions. This study addresses this gap by employing a random parameter probit model with heterogeneity in means and variances. Dataset comprises AV crash records obtained from the California Department of Motor Vehicles from 2018 to the first quarter of 2024. Crashes on roadway segments and intersection are modeled separately. Modeling results reveal that factors such as poor lighting conditions, braking maneuver of AVs, proceeding straight movement of HDVs, involvement of bikes/scooters, residential land-use significantly contribute to AV-involved crash occurrence on segments and at intersections. On segments, first quarter of the year, the retail/entertainment land use, sideswipe collision, dangerous maneuver of HDVs and proceeding straight moment of AVs affect the likelihood of AV-involved crashes. Meanwhile, at intersection, rear-end collision, raining/snowing, unusual road conditions, four-leg intersection, lack of pedestrian island/intersection control significantly increases the probability of AV-involved crashes while angle collision and large skew angle decreases it. The findings highlight the need for more targeted goals to improve AV’s safety, such as enhancing AV sensor perception capabilities, incorporating scenario-based tests by categorization of crash location, and developing mass education initiatives to facilitate the broader acceptance and understanding of AV technologies.