Adaptive Cruise Control Strategy Research Articles

Design of adaptive cruise control strategy for EREV considering driving behavior

Design of adaptive cruise control strategy for EREV considering driving behavior

Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons.

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon.

Vehicle adaptive cruise control strategy based on improved safe distance model for off-roads with changeable pavement and slope

Compared with the structured environment, off-road environment has complex and ever-changing road conditions. This paper is focused on the vehicle adaptive cruise task for vehicles driving on complex off-road terrain. Traditional ACC strategies do not take the complex road conditions into consideration, thus easily make the relative distance between vehicles unreasonable. To solve this problem, an off-road adaptive cruise control (OACC) strategy is proposed for off-roads with changeable pavement and slope. Firstly, the influence of road conditions and vehicle relative motion states on safe distance between vehicles have analyzed with the aim of developing a more reasonable space strategy for off-road conditions. Then, based on the analysis results, an improved safe distance model (ISDM) is proposed which take the influence factors into account comprehensively. The concept of road impact factor is proposed to prevent model degradation and effectively balance the influence of single road conditions and comprehensive factors on safe distance. Besides, target to improve the adaptability of ACC, a novel state space model has developed which can handle not only the change of vehicle motion states but also road conditions. In addition, the MPC-based OACC for off-road environment is proposed, which can enable the vehicle to better adapt to relative motion states and road conditions based on the novel state space model. Finally, the performance of OACC is verified by co-simulation in MATLAB/Simulink and Carsim, and a hardware-in-the-loop simulation system. Furthermore, the analysis of ISDM has conducted to illustrate the differences and similarities compared with the traditional safe distance models and to verify the effectiveness of ISDM. Simulation results show that the OACC and ISDM proposed in this paper have great performance in different off-road working conditions.

Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway

The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving (JAD) strategies on circular and linear scenarios have been evaluated. The manual vehicle is the intelligent driver model (IDM) and human driver model (HDM), respectively. The results suggest that on the ring road, the traffic performance of mixed traffic improves gradually with the increase of the proportion of CAVs under the ACC. Moreover, the traffic performance for the JAD strategy does not improve infinitely with the increase in the number of CAVs. Conversely, the FS strategy suppresses traffic waves at the cost of reducing traffic flow, and more CAVs are not beneficial for mixed traffic. It is interesting to note that under optimal performance in these three strategies, the FS strategy has the lowest number of CAVs, while the ACC strategy has the highest number of CAVs. For the linear road, it demonstrates that the JAD strategy exhibits a superior performance compared to the ACC. However, the FS strategy cannot dissipate traffic waves due to an insufficient buffer gap. Different models have varying effects on different strategies.

Ecological Driving Framework of Hybrid Electric Vehicle Based on Heterogeneous Multi-Agent Deep Reinforcement Learning

Hybrid Electric Vehicles have great potential to be discovered in terms of energy saving and emission reduction, and ecological driving provides theoretical guidance for giving full play to their advantages in real traffic scenarios. In order to implement ecological driving strategy with the lowest cost throughout life cycle in car-following scenario, the safety and comfort, fuel economy and battery health need to be considered, which is a complex nonlinear and multi-objective coupled optimization task. Therefore, a novel multi-agent deep deterministic policy gradient (MADDPG) based framework with two heterogeneous agents to optimize adaptive cruise control and energy management strategy respectively is proposed, thereby decoupling optimization objectives of different domains. Due to the asynchronous of multi agents, different learning rate schedules are analyzed to coordinate learning process to optimize training results. And an improvement on Prioritized Experience Replay technique is proposed, which improves optimization performance of original MADDPG method by more than 10%. Simulations under mixed driving cycles show that, on the premise of ensuring car-following performance, the overall driving cost including fuel consumption and battery health degradation of MADDPG-based method can reach 93.88% of that of DP. And the proposed algorithm has good adaptability to different driving conditions.

Energy-optimized adaptive cruise control strategy design at intersection for electric vehicles based on speed planning

Energy-optimized adaptive cruise control strategy design at intersection for electric vehicles based on speed planning

Integrated velocity optimization and energy management for FCHEV: An eco-driving approach based on deep reinforcement learning

Ecological driving (eco-driving) is a promising technology for transportation sector to save energy and reduce emission, which works by improving vehicle behaviors in traffic scenarios. Fuel cell hybrid electric vehicles (FCHEV) are receiving extensive attentions due to global fossil energy crisis, but whose implementations for eco-driving result in multiple objective collaborative optimization problems. In this paper, an eco-driving framework for FCHEV is proposed based on deep deterministic policy gradient (DDPG) algorithm. And it combines adaptive cruise control (ACC) and energy management strategy (EMS) into an integrated architecture. Firstly, in order to achieve excellent balance between driving behaviors and fuel economy, an appropriate weight coefficient value is determined after adequate explorations. Secondly, power-varying equivalent hydrogen conversion coefficient function is constructed to save fuel consumption by 8.97%. Thirdly, ablation experiments for health state of fuel cell system present 19.95% decrease in terms of health degradation. Then, comparison experiments indicate that the DDPG-based eco-driving strategy can reach 94.16% of that of dynamic programming with respect to equivalent hydrogen consumption, meanwhile with best ride comfortability. Moreover, simulation results under validation driving cycle manifest its excellent adaptability.

Collaborative adaptive cruise control and energy management strategy for extended-range electric logistics van platoon

This paper improves the economy of the extended-range electric logistics van (ERELV) platoon from two aspects of cooperative adaptive cruise control (CACC) and energy management strategy (EMS). Based on the vehicle-to-everything (V2X) communication, to improve the economy of heterogeneous vehicle platoon CACC system, a distributed model predictive controller (DMPC) with stability, comfort, and the economy as optimization goals are designed. The sufficient conditions for the asymptotic stability of the vehicle platoon closed-loop system are obtained by Lyapunov stability analysis. The multi-agent deep reinforcement learning (MADRL) algorithm is used to solve the EMS of the ERELV platoon. Under the framework of centralized training distributed execution (CTDE), the experience of all agents can be obtained during training, and the actions can be output only according to their local observation states during execution. The simulation results show that the designed ecological cooperative adaptive cruise control (Eco-CACC) effectively balances the stability and economy of a heterogeneous vehicle platoon. Taking dynamic programming (DP) as the benchmark, compared with the single-agent algorithm, EMS based on a multi-agent deep deterministic strategy gradient (MADDPG) algorithm can achieve a near-optimal solution while significantly improving the learning efficiency.

V2V‐enabled cooperative adaptive cruise control strategy for improving driving safety and travel efficiency of semi‐automated vehicle fleet

AbstractAlthough highly automated vehicle fleets have committed a safe and efficient traffic flow, the next few years are likely the human‐driven vehicles (HDV) and intelligent vehicles (IV) mixed in large‐scale traffic network, where the interaction of HDVs with IVs will become more frequent. In this study, the authors investigate the traffic flow performance in a widespread of V2V environment deployed for all types of HDVs and IVs, levelled from L0 to L5. Based on this assumption, the vehicle movements and operations are collected and assembled into a V2X BSM dataset, and to any vehicle in fleet, the sequential movements and driving behaviours of the vehicle ahead can be predicted in the short term and considered as the input to cooperative adaptive cruise control (CACC) model of the controlled vehicle. The predicted motion of the vehicle ahead is applied to rolling optimization process of a given MPC framework, which calculates appropriate longitudinal operation for each car‐following vehicle and keeps optimal equilibrium state of the vehicle fleets in heterogeneous traffic flow. The experimental results showed that when the front vehicle generates random acceleration/deceleration in saturated and over saturated traffic state, in the car‐following case, the improved CACC controller maintains average car‐following distance at 20–40 m, the acceleration variation in the range [−0.1, 0.25], the gap error changed within [−10, 13], and controls the relative speed variation from −0.25 to 0.3 m/s, compared to an average headway of 20–140 m, acceleration variation in the range [−1.6, 0.65], gap error changed within [−21, 30], and a relative speed variation from −1.75 to 4.1 m/s by the traditional CACC controller. In the car‐in and car‐out cases, the vehicle controlled by the improved CACC controller had a travel displacement of 4050 m, a speed fluctuation interval of [18.7, 27.7], and an acceleration variation range of [−0.15, 0.2], while the vehicle controlled by the traditional MPC controller had a travel displacement of 3800 m, a speed fluctuation interval of [13.8,27.7] and an acceleration variation range of [−2.9, 0.6]. The improved CACC model can adapt more accurately and quickly to the maneouvre of vehicle ahead in V2V environment, which can effectively improve the capacity of heterogeneous traffic flow and the safety and comfort of driving.

Research on a Multimode Adaptive Cruise Control Strategy with Emergency Lane-Changing Function

In emergency situations, it is difficult to meet the requirements of safe driving only by relying on the braking system, and the probability of accidents can be reduced by employing an emergency lane-changing mode. To improve the adaptability of the distributed electric vehicle adaptive cruise control (ACC) strategy to complicated and volatile conditions, a multimode ACC strategy with emergency lane-changing function is proposed. Firstly, the ACC is divided into four modes aimed at the problem of complex conditions, and a switching strategy is designed to control the switching of them. Simultaneously, the car-following mode is divided in greater detail based on time to collision (TTC), and the acceleration weighted average algorithm is adopted for accuracy and output continuity during switching. Then, the ACC is established with a hierarchical control framework, in which a PID-based cruise mode and a multi-objective optimized car-following mode based on model predictive control (MPC) are devised. The target brake wheel cylinder pressure is selected as the emergency brake pressure in takeover mode. In addition to the MPC-based system, the emergency lane-changing mode incorporates a yaw moment controller in the upper-level controller to improve body stability during emergency lane changing in the upper-level controller. In the lower-level controller, the upper-level output is converted into driving torque, wheel cylinder pressure, and front wheel angle to control vehicle travel and generate additional yaw moment. Finally, the results indicate that the presented multimode switching strategy can adapt to complex and instable transportation environments. In the cruise control scenario, the host vehicle can rapidly reach cruising speed within 5 s. In the car-following scenario, the host vehicle can stably follow the preceding vehicle with an acceleration of −5–3.5 m/s2 and a jerk of −2–2 m/s3 throughout the entire process, maintaining a safe distance from the preceding vehicle. In emergency lane-changing scenarios, vehicles with body stability control can better follow the lane-changing trajectory, and tracking accuracy is improved by 65%. Simultaneously, parameters such as front wheel angle, yaw rate, sideslip rate, and lateral acceleration remain within the normal range. In mixed switching scenarios, each mode can be correctly switched according to diverse operating conditions, and obstacle avoidance can be accomplished through horizontal and vertical strategies, which also verify the effectiveness and rationality of the control strategy proposed.

Lane-changing control for hybrid electric vehicles with dedicated hybrid transmission based on robust model predictive control

Car-following and lane-changing are extremely important for vehicles. The traditional adaptive cruise control (ACC) strategy has various drawbacks due to the complicated driving conditions. A new control strategy of robust model predictive control (RMPC) for car-following and lane-changing is proposed based on a new type of hybrid electric vehicle (HEV) with dedicated hybrid transmission (DHT-HEV). The control model includes a vehicle model, a following and lane-changing model and an RMPC controller. The vehicle model integrates the DHT-HEV and dynamic models with a longitudinal dynamic model, seven degrees of freedom (DOF) vehicle dynamic model and three DOF control model. The following and lane-changing model is described as car-following and lane-changing models of two-quintic function. The RMPC controller is used to balance the control of vehicle longitudinal and lateral dynamics with the lateral acceleration disturbance. Simulation results demonstrate that the RMPC controller can track the reference trajectory during two lane-changing process when the test scenarios of different accelerations are set compared with the conventional ACC control. Different Np (predictive horizon) and Nc (control horizon) are also analysed to improve the solving performance of RMPC. Results indicate that the solving performance of the system is optimal when Np = 30 and Nc = 1. Furthermore, different weight coefficient matrixes ( Q and R) are taken into the consideration by coordinating the car-following performance and lateral stability. Accordingly, the controllers of RMPC-LC, RMPC-ACC and RMPC-LA are set, demonstrating that the control of RMPC-LA best balances two aspects and is always within a safe car-following distance. Meanwhile, the hardware in the loop (HIL) is utilised to verify the effectiveness of the proposed RMPC control strategy, which comprises model compilation, the establishment connection between the D2P ECU and the simulator and control input.

Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics

This paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and the parameters in the equivalent circuit model are updated according to the actual capacity of the battery. The controller uses indicators that characterize driving safety, tracking performance, comfort, and economy. The economic indicator is the decrease in the value of the battery capacity. Fuzzy weight allocation is designed based on the host vehicle’s speed and the workshop’s relative distance to adjust the weight between different indicators under different working conditions. Additionally, the proposed controller is compared with other traditional controllers under different working conditions, cycle times, and battery state of health (SOH). The simulation results indicate that, under various battery SOH conditions, the performance of the controller which considers battery capacity degradation characteristics is better than that of traditional controllers. Moreover, the fixed-weight controller performs better when following a vehicle at medium and low speeds. Finally, the proposed strategy was validated through hardware-in-the-loop testing, demonstrating its ability to meet the real-time requirements of the system.

Multiobjective Optimization of Safety, Comfort, Fuel Economy, and Power Sources Durability for FCHEV in Car-Following Scenarios

Safety, comfort, and energy-saving oriented car-following issue for fuel cell/battery hybrid electric vehicle (FCHEV) is a comprehensive problem of vehicle dynamic and fuel economy. Combining adaptive cruise control (ACC) and energy management strategy (EMS) is proven to be one promising method to realize multiobjective co-optimization. However, in majority of existing researches, degradation of power sources is always ignored and trade-off among different objectives remains a burning issue, in this article, a Pareto-based EMS under car-following scenarios is proposed. Specifically, degradation models of fuel cell/battery are established and incorporated into multiobjective optimization functions. Then, based on back-stepping technique and equivalent consumption minimization strategy (ECMS), an integrated framework of ACC and EMS is developed, which realizes the coordination between vehicle external longitudinal dynamics control and internal powertrain energy management. For getting the trade-off among the abovementioned objectives, a Pareto-involved multiobjective optimization method is proposed to optimize control parameters of the integrated framework of ACC and EMS. The simulation results of Urban Dynamometer Driving Schedule (UDDS) test highlight that compared with the weighted-sum methods, the proposed method can utmost reduce average tracking error by 15.14%, variation of speed by 8.61%, equivalent fuel consumption cost by 7.56%, and comprehensive power sources degradation cost by 17.78%.

Energy-Saving Optimization for Electric Vehicles in Car-Following Scenarios Based on Model Predictive Control

In this paper, an economy-oriented car-following control (EOCFC) strategy is proposed for electric vehicles in car-following scenarios. Specifically, a controller based on model predictive control (MPC) is developed to optimize the host vehicle’s speed for better energy economy while ensuring good car-following performance and ride comfort. The vehicle’s energy consumption is accurately quantified in the form of demand power, which is incorporated in the cost function for energy optimization. The proposed EOCFC strategy is evaluated using three standard test cycles, i.e., New European Driving Cycle (NEDC), Urban Dynamometer Driving Schedule (UDDS) and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), in comparison with a typical multi-objective adaptive cruise control strategy. The evaluation results demonstrate that the proposed EOCFC improves the energy economy of the host vehicle by 0.53%, 3.33% and 1.51%, under the NEDC, UDDS and WLTC test cycles respectively.

Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios

In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. To control a host vehicle in an energy-efficient manner using model predictive control (MPC), and moreover, enhance the performance of an ecological adaptive cruise control (EACC) strategy, forecasting the future velocities of a target vehicle is essential. For this purpose, a deep recurrent neural network-based vehicle speed prediction using long-short term memory (LSTM) and gated recurrent units (GRU) is studied in this work. Besides these, the physics-based constant velocity (CV) and constant acceleration (CA) models are discussed. The sequential time series data for training (e.g. speed trajectories of the target and its preceding vehicles obtained through vehicle-to-vehicle (V2V) communication, road speed limits, traffic light current and future phases collected using vehicle-to-infrastructure (V2I) communication) is gathered from both urban and highway networks created in the microscopic traffic simulator SUMO. The proposed speed prediction models are evaluated for long-term predictions (up to 10 s) of target vehicle future velocities. Moreover, the results revealed that the LSTM-based speed predictor outperformed other models in terms of achieving better prediction accuracy on unseen test datasets, and thereby showcasing better generalization ability. Furthermore, the performance of EACC-equipped host car on the predicted velocities is evaluated, and its energy-saving benefits for different prediction horizons are presented.

Optimization Based Adaptive Cruise Control and Energy Management Strategy for Connected and Automated FCHEV

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m.

Cooperative Formation of Autonomous Vehicles in Mixed Traffic Flow: Beyond Platooning

Cooperative formation and control of autonomous vehicles (AVs) promise increased efficiency and safety on public roads. In mixed traffic flow consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation. In this paper, we investigate how different formations of AVs impact traffic performance from a set-function optimization perspective. We first reveal a stability invariance property and a diminishing improvement property of noncooperative formation when AVs adopt typical Adaptive Cruise Control (ACC) strategies. Then, we focus on the case of cooperative formation where the AV controllers are cooperatively designed %redesign the control strategies of AVs in different formations and investigate the optimal formation of multiple AVs using set-function optimization. Two predominant optimal formations, i.e., uniform distribution and platoon formation, emerge from extensive numerical experiments. Interestingly, platooning might have the least potential to improve traffic performance when HDVs have poor string stability behavior. These results suggest more opportunities for cooperative formation of AVs, beyond platooning, in practical mixed traffic flow.

Finite-Time Stabilization of a Collection of Connected Vehicles Subject to Communication Interruptions

This paper investigates a platooning problem of connected vehicles subject to communication interruptions and latency. For a heterogeneous platoon of vehicles, a hybrid reference model with cooperative adaptive cruise control (CACC) and adaptive cruise control (ACC) is established. Then a novel CACC-ACC switching control method is suggested, which activates either a CACC scheme or an augmented ACC strategy depending on the status of communications. By introducing a platoon state tracking error system, a control algorithm is derived using finite-time sliding-mode control theory, which can robustly guarantee string stability and zero steady-state spacing error of the vehicular platoon. Simulations have shown the effectiveness and superiority of the method.

Energy-oriented car-following control for a front- and rear-independent-drive electric vehicle platoon

In this paper, a cooperative multi-objective platoon control (CMOPC) strategy is proposed for a front- and rear-independent-drive electric vehicle (FRIDEV) platoon. Two major measures are taken in this approach to enhance platoon economy. Firstly, a novel multi-objective nonlinear model predictive control (NMPC) system is devised by combining car-following control for connected and automated vehicle platoons and torque distribution control for FRIDEVs to exploit the energy-saving potential of an FRIDEV platoon. Secondly, the total power of all vehicles in the platoon is considered in the cost function to better reflect the overall energy consumption of the platoon. The proposed CMOPC strategy is evaluated under three typical driving cycles, i.e. UDDS, WLTC and HWFET; the ecological cooperative adaptive cruise control strategy is employed as the benchmark of evaluation. The comparative simulation results demonstrate that the proposed CMOPC not only ensures equivalent car-following performance, but also improves the platoon economy by 3.0%, 1.6% and 4.7% respectively under UDDS, WLTC and HWFET driving cycles.

A variable weight adaptive cruise control strategy based on lane change recognition of leading vehicle

The traditional adaptive cruise system is responsible for delay in recognizing the cut-in/cut-out behaviour of front vehicle, and there is significant longitudinal acceleration of the vehicle fluctuation leading to reduced driver’s comfort level and even dangerous situation. In this paper, the next generation simulation data set and back propagation (BP) neural network are used to train the vehicle lane change recognition model to recognize the lane change behaviour of the preceding vehicle. The higher controller adopts variable weight linear quadratic optimal control to adjust the weight parameters according to the recognition results of front vehicle to reduce the fluctuation of vehicle acceleration. The lower layer adopts fuzzy proportional-integral-derivative (PID) control to follow the expected acceleration and builds the vehicle inverse dynamic model. Through CarSim/Simulink co-simulation, the results show that, under the cut-in or cut-out and working conditions, the behaviour of the leading vehicle can be recognized, following target can be switched in advance, weight parameters can be adjusted and the large fluctuation of longitudinal acceleration can be reduced.