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BackgroundSurging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions.MethodsTractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives.ResultsOur data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%).ConclusionsIn general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

With the application of mobile communication technology in the automotive industry, intelligent connected vehicles equipped with communication and sensing devices have been rapidly promoted. The road and traffic information perceived by intelligent vehicles has important potential application value, especially for improving the energy-saving and safe-driving of vehicles as well as the efficient operation of traffic. Therefore, a type of vehicle control technology called predictive cruise control (PCC) has become a hot research topic. It fully taps the perceived or predicted environmental information to carry out predictive cruise control of vehicles and improves the comprehensive performance of the vehicle-road system. Most existing reviews focus on the economical driving of vehicles, but few scholars have conducted a comprehensive survey of PCC from theory to the status quo. In this paper, the methods and advances of PCC technologies are reviewed comprehensively by investigating the global literature, and typical applications under a cloud control system (CCS) are proposed. Firstly, the methodology of PCC is generally introduced. Then according to typical scenarios, the PCC-related research is deeply surveyed, including freeway and urban traffic scenarios involving traditional vehicles, new energy vehicles, intelligent vehicles, and multi-vehicle platoons. Finally, the general architecture and three typical applications of the cloud control system (CCS) on PCC are briefly introduced, and the prospect and future trends of PCC are proposed.

One of the contemporary trends is to emphasize the benefits of using hybrid drives over conventional ones. Due to the growing popularity of this category of drives, research on the optimization of energy consumption is becoming increasingly popular. The paper presents research on a selected plug-in hybrid drive unit of one of the most popular vehicle manufacturers in the European Union. The research process itself was based on the assessment of the use of active DISTRONIC cruise control as a tool that can influence the optimization of energy consumption of the tested drive unit. The experiment was conducted in real road conditions. Analyses were carried out for several drive system configuration modes: hybrid, E-mode, E-save and charging. The results of the experiment indicated the most optimal drive setting mode on expressways. These analyses emphasize the significant role of intelligent systems as a tool that allows for the optimization of energy consumption of vehicles equipped with plug-in hybrid units. The presented research is dedicated to users of this category of vehicles, while at the same time providing an answer to the question of how to effectively use the described drive unit in real operating conditions.

In this work, we propose a real proportional-integral-derivative plus second-order derivative (PIDD 2 ) controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation. In this regard, this paper is the first report in the literature demonstrating the implementation of a real PIDD 2 controller for controlling the respective system. We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism. We also propose a simple yet effective objective function to increase the performance of the proposed algorithm (CmOBL-AO) to adjust the real PIDD2 controller's parameters effectively. We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. For the vehicle cruise control system, we confirm the more excellent performance of the proposed method against particle swarm, gray wolf, salp swarm, and original Aquila optimizers using statistical, Wilcoxon signed-rank, time response, robustness, and disturbance rejection analyses. We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD 2 controller from a wider perspective. The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds. Lastly, we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases. We show the CmOBL-AO-based real PIDD 2 controller as the most efficient method to control a vehicle cruise control system.

Connected and automated vehicles (CAVs) have great potential to improve driving safety. A basic performance evaluation criterion of CAVs is whether they can drive more safely than human drivers in real traffic scenarios. This study proposes a method to optimize longitudinal control model parameters of CAVs using empirical trajectory data of human drivers in risky car-following scenarios. Firstly, the initial car-following pairs (I-CFP) are extracted from empirical trajectory data. Then, two types of real longitudinal control models of CAVs, the adaptive cruise control (ACC) and the cooperative ACC (CACC) control models, are employed for simulation in the car-following scenarios with default parameter values, which generate original trajectories of simulated car-following pairs (S-CFP). Finally, a genetic algorithm (GA) is applied to optimize control model parameters of ACC and CACC vehicles and generate optimized trajectories of car-following pairs (O-CFP). Results indicate that safety condition of S-CFP is better than that of I-CFP, while the O-CFP has the best safety performance. The optimized parameters in the ACC/CACC models are diverse and different from the default parameters, indicating that the best model parameters vary with different car-following scenarios. Findings of this study provide a valuable perspective to reduce the rear-end collision risks.

Designing the control structures of fractional order PID controllers has proven to be effective in providing adaptability in set point tracing the performance of a nonlinear cruise control system. Wheel rolling resistance, wind drag force, and road gradient are incorporated into the design to better describe the system under consideration and to show how the nonlinear cruise control system behaves. This study presents a comparative investigation using simulation between control structures such as fractional order proportional–integral–derivative, fractional order integral minus proportional derivative, and fractional order proportional integral minus derivative. By preserving integral error indices as the goal function, a genetic algorithm is used to improve the controller gain parameters and fractional scaling values. To prevent integral windup conflicts and derivative boost issues, both traditional fractional order structures and adaptive neuro-fuzzy-based fractional order structures were used to create the adaptive cruise control system. The FO ANFIS PD plus I controller for the cruise control system exceeds the competition in servo and regulatory difficulties.

Advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC), have been recently installed in passenger cars. Although the safety performance of these systems is limited in high-risk scenarios, some drivers overtrust the system and perform secondary tasks. Previous research indicated that drivers using ADAS tend to become distracted compared with manual driving. In contrast, the use of ACC has been reported to reduce the collision rate on highways by about half. This study aimed to clarify the mechanism of the effect of ACC on driver behavior and consequently mitigate accidents. Our previous experiments showed that driver reaction time to perform avoidance behaviors in high-risk scenes is shortened when using ACC, even if the driver is distracted. This paper first aims to elucidate the factors influencing driver risk-avoidance strategies in a potentially critical frontal collision scenario. The hypothesis is that the driver’s perception of tactile vehicle motion, accompanied by the deceleration of ACC active intervention, prompts risk awareness and avoidance. The hypothesis was verified through analysis of driver gaze movement and brake operation behavior in critical scenarios using driving simulator experiments. Based on the obtained results, the advanced driver assistance system longitudinal control laws adapted to the driver’s eyes-off state are proposed based on the high-risk scenarios. Finally, the driver acceptance and ability to reduce the risk of the proposed system were quantitatively evaluated using a driving simulator.

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.

Reducing energy consumption and carbon emissions while effectively utilizing automotive resources is a crucial task for both the country and the automotive industry. To achieve this goal, this study employs the Dynamic Programming algorithm to optimize the control of pure electric commercial vehicles' driving process, thereby reducing their energy consumption. With the objective function of minimizing energy consumption and the constraints of vehicle power performance and economy, a simulation platform is built to analyze the driving energy consumption of pure electric commercial vehicles under typical working conditions. The experiment verified the effectiveness and feasibility of the driving energy consumption control strategy based on the Dynamic Programming algorithm. The results showed that compared with the traditional PID control method, the improved Dynamic Programming algorithm saved 1.63%, 4.56%, 7.19%, and 4.63% of the power under the conditions of uphill, downhill, first uphill then downhill, and first downhill then uphill, respectively. Compared with traditional Dynamic Programming algorithms, the improved algorithm saved power by 0.49%, 3.23%, 6.68%, and 2.36%, respectively. Compared to normal driving, the optimized-speed tracking reduced total energy consumption by 23.56%, while energy consumption during constant-speed driving decreased by 6.62%. This indicates that the proposed energy consumption control strategy for pure electric commercial vehicles can achieve the goal of reducing driving energy consumption. The proposed driving energy consumption control strategy for pure electric commercial vehicles aims to plan the vehicle's driving speed, enabling it to travel on a reasonable speed track, and ultimately reducing driving energy consumption while improving driving economy.

A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized car-following according to the driver’s driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller.