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Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience.

Abstract Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

This paper presents an experimental investigation of human control of vehicles carried out on the basis of general theories on human movement. The longitudinal and lateral accelerations are studied, and their relations with theories of motor optimality principles, such as minimum jerk, minimum variance, and the two-thirds power law are highlighted. Data have been collected during the final experimental phase of the EU interactIVe project, in which a vehicle developed by Centro Ricerche Fiat has been used to demonstrate driver continuous support produced by an artificial co-driver, within a shared initiative framework. 24 subjects drove the vehicle on a test route twice: once with the system active, the other with the system silent. The test route is composed of urban arterials, extra urban and motorway roads, and takes approximately 40–45 min to be driven. The total database thus amounts to ~35 h of driving data recordings, for a total of ~1.2 M samples per signal. Statistical summary data are presented, which describe human preferred accelerations, correlation between acceleration, curvature, and speed, and between longitudinal and lateral acceleration. Different driving modalities, corresponding to different motor strategies and primitives, are revealed. Comparisons with literature data are also made and discussed. The summary statistics may be useful for the design of future ADAS systems, and indeed they have been collected for the final tuning of the interactIVe co-driver.

Parking an articulated vehicle is a challenging task that requires skill, experience, and visibility from the driver. An automatic parking system for articulated vehicles can make this task easier and more efficient. This article proposes a novel method that finds an optimal path and controls the vehicle with an innovative method while considering its kinematics and environmental constraints and attempts to mathematically explain the behavior of a driver who can perform a complex scenario, called the articulated vehicle park maneuver, without falling into the jackknifing phenomena. In other words, the proposed method models how drivers park articulated vehicles in difficult situations, using different sub-scenarios and mathematical models. It also uses soft computing methods: the ANFIS-FCM, because this method has proven to be a powerful tool for managing uncertain and incomplete data in learning and inference tasks, such as learning from simulations, handling uncertainty, and capturing expert parking expertise. The results obtained from the proposed method show that the use of a soft computation method significantly reduces the cumulative errors: errors resulting from summing up each sub-maneuver. Of course, the main source of these errors is related to starting from the random point that exists at the beginning of the predefined complex scenario. This implies that our method can effectively handle the uncertainty and variability of parking scenarios.

The tightening of emission standards and homologation rules lead car manufacturers to rely on simulation testing in early development phases. Coupling an engine to a testbench controlled by a real-time simulation environment allows flexible, reliable, and reproducible testing for consumption and emission studies. However, interest in this method referred to as engine-in-the-loop (EiL) is relatively recent and few details can be found regarding the simulation environment. Following previous work, this study details a driver model based on the PI structure and augmented with preview and anti-windup. The focus is set on a conventional powertrain with a manual transmission for which the driver must also manage the clutch pedal during gearshift and take-off phases. Extended analysis of vehicle tests allows defining the driver’s behavior during these phases for different profiles. The driver model is then tested in the EiL environment and the impact of the gearshift profile on fuel consumption and pollutant emissions can be assessed. Besides the slight increase in fuel consumption, results show that increasing the gearshift duration degrades the regulation of the richness by the ECU, thus increasing CO engine-out emissions as well as decreasing NOx emissions. Finally, results suggest that a longer gearshift also affects the catalyst efficiency, which results in higher NOx tailpipe emissions.

This paper proposes a subjective risk‐driven driver behavior modeling approach that incorporates drivers’ risk perception into decision‐making. Inspired by cognitive science, the proposed framework decomposes drivers’ internal evaluation into preference, reward, and subjective risk constraint. The subjective risk is defined as the coupling between drivers’ perceived uncertainty and environmental cost. To obtain a differentiable representation of the subjective risk, we adopt a unified Gaussian potential field formulation that couples the drivers’ cognitive risk fields and environmental cost risk fields through a Gaussian overlap integral. Building upon this, a risk‐threshold Maximum Entropy Inverse Reinforcement Learning paradigm is developed to learn drivers’ internal preference reward and risk perception from demonstrations. Experimental results on large‐scale naturalistic driving data demonstrate that, compared with baseline methods, the proposed approach can stably learn policies that are more consistent with real human driving decision distributions, while maintaining strong performance in high‐risk scenarios. Further human‐in‐the‐loop experiments confirm its effectiveness in personalized modeling, achieving consistency with individual driving behavior across kinematic characteristics, decision‐making patterns, and subjective risk perception. In addition, qualitative analyses show that the learned subjective risk field provides an interpretable representation of drivers’ risk perception, revealing how perceived risk influences driving decision‐making processes.

The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.

The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.