Adaptive Cruise Control Algorithm Research Articles

Adaptive Cruise Control Based on Safe Deep Reinforcement Learning.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

Research on collaborative adaptive cruise control based on MPC and improved spacing policy

A Cooperative Adaptive Cruise Control (CACC) algorithm based on Model Predictive Control (MPC) and an improved spacing policy is proposed in this study to address the current issues of low road utilization and inadequate dynamic regulation during platoon driving. First and foremost, the state of the leader vehicle and the minimum safe following distance are incorporated into the spacing policy to create an enhanced constant time headway (CTH) spacing policy. Secondly, following the MPC principle, the optimization problem of vehicle platoon is converted into a constrained quadratic programming problem that fulfills the requirements of driving safety, following distance, and ride comfort in a platoon. Finally, six-homogeneous-vehicle platoon is constructed for simulation verification, and the results show that the designed algorithm can not only ensure the string stability of platoon, but also effectively improve the road utilization rate. And in the vehicle of platoon cut-in/cut-out conditions, CACC is proven to have good ability of dynamically adjusting and restoring platoon stability.

An Improved Advanced Driver-Assistance System: Model-Free Prescribed Performance Adaptive Cruise Control

To maintain a safe distance between the autonomous vehicle and the leader, ensure that the vehicle runs at its expected speed as far as possible, and achieve various control requirements such as speed, distance and collision avoidance, a model-free prescribed performance adaptive cruise control (ACC) algorithm based on funnel control is proposed. The contributions of this paper are that the designed ACC algorithm only requires the speed and position information and can constrain their tracking errors within a predetermined range. When the follower is far away from the leader, the speed-prescribed performance controller adjusts the follower vehicle’s speed to the reference velocity. When the follower vehicle approaches the leader vehicle, a distance-prescribed performance controller is designed to adjust the distance between the follower and the leader. On this basis, the prescribed performance function can expand the switching interval, thereby improving the robustness of the speed and distance control switching process. The effectiveness of the designed algorithm is demonstrated in three scenarios, such as approaching and following, emergency braking, and frequent starting and stopping. The results show that during the speed control stage, the designed algorithm allows the vehicle’s operating speed to vary within a predetermined spatial range; in the distance control stage, the designed algorithm strictly limits the distance error within the preset range. The speed and distance of the vehicle change smoothly, and there is no overshoot during the initial state adjustment, emergency braking, and frequent start and stop stages, demonstrating a good control effect.

Centralized model‐predictive cooperative and adaptive cruise control of automated vehicle platoons in urban traffic environments

AbstractRecent research has demonstrated that cooperative and automated driving functions can improve traffic efficiency and safety. Traffic efficiency can be increased and the overall vehicle energy consumption can be reduced through cooperative driving functions not only on the highway, but also in urban areas. To this end, this paper presents a linear model predictive control (MPC) algorithm for the optimization of velocity trajectories of vehicles in a platoon in an urban traffic environment. A novel centralized approach is implemented to optimize the total energy consumption of all vehicles while improving traffic efficiency. To solve the optimal control problem, for the first time, an additional data‐driven velocity prediction model of the vehicle in front of the platoon is used to consider the influence of realistic prediction errors when evaluating the developed control strategy in a vehicle simulation environment. The simulated total energy consumption is compared with a rule‐based cooperative adaptive cruise control (CACC) algorithm. Furthermore, the traffic density and the platoon string stability, the robustness and computer time of the centralized approach are also included in the evaluation. Simulation results indicate that, considering all evaluation criteria, the centralized cooperative control strategy can improve energy efficiency by up to 15% in the investigated urban scenario.

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions.

Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.

A Literature Review of Energy Optimal Adaptive Cruise Control Algorithms

Transportation sector and its impacts on climate change have received much attention over the last decade. Energy-optimal vehicle control algorithms such as adaptive cruise control can potentially reduce fuel consumption and the consequential environmental impacts. Adaptive cruise control algorithms optimize vehicles’ speed to lower energy consumption considering several constraints, including safety, stability, and comfortability. A wide range of algorithms with different objectives and optimization mechanisms have been reported in the literature. Due to the high diversity of constraints and objectives, a comprehensive study of these algorithms is required. In recent literature reviews, there is no comprehensive summary of the adaptive cruise control algorithms’ features, classes, and objectives. This paper presents a holistic literature review of energy-optimal adaptive cruise control algorithms. We gathered relevant publications from well-eminent journals. Information on diversity and the number of publications will be presented to provide data on used references. For each paper, the objectives, features, and relevant classes of algorithms were extracted. Then a classification based on objectives is suggested, and mathematical formulas of representative studies are summarized to provide the required knowledge regarding mathematical modeling and optimization in this field. The study provides a useful insight into the development of the cruise control systems research field, revealing those scientific actors (i.e., authors, developers, and institutions) that have made the biggest research contribution to its development.

Model-Based Design Approach for Validation of Vehicle Longitudinal Control Algorithm

In this paper, model-based testing strategies are described for the validation of an Adaptive Cruise Control (ACC) algorithm developed for a 2019 Chevrolet Blazer as part of the EcoCAR Mobility Challenge. A team of undergraduate and graduate students developed testing procedures to assess model fidelity, and to identify and resolve issues with the algorithm before deployment to a student-modified production vehicle. The algorithm validation is conducted via three progressive levels of validation environments: Model-In-the Loop (MIL), Hardware-In-the-Loop (HIL), and Driver-In-the-Loop (DIL). When the ACC algorithm is evaluated using system requirements in the testing sequence, the MIL environment performs the tests at least 87% faster than the HIL environment. The MIL environment can also utilize parallel computing, which leverages multi-core CPUs to conduct multiple simulations simultaneously. Although comparisons between MIL and HIL results revealed good agreements, slight differences in system dynamics highlights a need for future Vehicle-In-the-Loop (VIL) testing. By showing how the concepts can be applied to the validation of an autonomous feature in a vehicle with detailed test scenarios and evaluation metrics, the paper will serve as a good reference for the students and engineers interested in this field.

Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory and experiments

Commercial adaptive cruise control (ACC) systems are bi-level: an upper-level planner decides the target trajectory and the low-level system executes it. Existing literature on ACCs mostly focus on the planner algorithms or the actuator delay, while the transition process between them, e.g. the low-level control design and its impact are often ignored. This paper tries to fill this gap by digging into the codebase of a recent open-source self-driving system, Openpilot (OP), Comma.ai, from which we extract and formulate the algorithms at both the upper and lower levels. For linear ACCs, the paper extends the transfer function analysis from planners only to full control loops and investigates the impact of slow/fast low-level control on the overall string stability (SS). For MPC ACCs, it studies their planning characteristics based its optimization objectives and approximates the low-level impact using an ODE approach.We find that low-level control has a significant impact on the overall SS of ACCs: (i) slow low-level control undermines SS under small frequencies and improves SS given large frequencies for linear systems, (ii) MPC features a varying gain throughout an oscillation, where the fast low-level control typically results in a ‘fast-slow’ changing process of the MPC gain, which benefits the SS, whereas the slow low-level control leads to a ‘slow-fast’ varying gain which undermines the SS, (iii) slow low-level control are common as they arise from comfort-oriented control gains, from a ”weak” actuator performance or both, and (iv) the SS is very sensitive to the integral gain under slow low-level control for both PI and PIF controllers. Overall, the study recommends fast low-level control for ensuring vehicular SS to reduce traffic congestion, considering that large congestion waves usually feature both small frequencies and large amplitudes, although slow controllers could perform even better provided a short and small leader perturbation. The findings of this paper are verified both numerically and experimentally. For the first time in the literature we implement custom ACC algorithms on market cars, and achieve SS on open roads with a random leader by only tuning the low-level controllers. The source code is shared at https://github.com/HaoZhouGT/openpilot to support on-road experiments of arbitrary car-following models, which may be of interest to other studies.

Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems

This paper investigates the accuracy and robustness of car-following (CF) and adaptive cruise control (ACC) models in reproducing measured trajectories of commercial ACCs. To this aim, a general modelling framework is proposed, in which ACC and CF models have been incrementally augmented with physics-based extensions: namely, perception delay, linear or nonlinear vehicle dynamics, and acceleration constraints. This framework has been applied to the Intelligent Driver Model (IDM), Gipps’ model, and to three basic ACC algorithms. These are linear controllers which are coupled with a constant time-headway spacing policy, and with two other policies derived from the traffic flow theory: the IDM desired distance function, and Gipps’ equilibrium distance-speed function. The ninety models resulting from the combination of the five base models with the aforementioned extensions, have been assessed and compared through a vast calibration and validation experiment against measured trajectory data of vehicles driven by ACC systems. Overall, the study has shown that physics-based extensions provide limited improvements to the accuracy of existing models. In addition, if an investigation against measured data is not carried out, it is not possible to argue which extension is the most suited for a specific model. The linear controller with Gipps’ spacing policy has resulted the most accurate model, while the IDM the most robust to different input trajectories. Eventually, all models have failed to capture the behaviour of some car brands – just as models fail with some human drivers. Therefore, the choice of the “best” model is independent of the car brand to simulate.

Design Methodology to Derive Over-Damped String Stable Adaptive Cruise Control Systems

In this paper, we formulate a common design methodology to derive an adaptive cruise control algorithm in order to compensate the effect of the lower level time-lag on the stability. Here the dynamics of the vehicle is approximated by a first order system. The lower level time-lag is referred to the lag associated with the vehicle dynamics to attain the desired condition computed by the adaptive cruise controller. Inclusion of such dynamics reduces the performance of an adaptive cruise control system, both locally and collectively. The solutions presented here can be applied to design a new adaptive cruise control model or to extend any model with velocity dependent spacing function. The derived design method fulfills the over-damped string stability condition in presence of a time-lag in the plant, irrespective of the choice of the equilibrium operating point of linearization. The over-damped characteristic improves both safety and performance. To demonstrate the application of the presented method, two existing models are extended using the derived strategy, viz. the variable time gap and the quadratic range policy. Later the extended models are simulated with rigorous driving conditions, in order to illustrate the improvements in comparison with the respective original models as well as with the extended models using an existing compensation strategy.

Research on adaptive cruise control algorithm considering road conditions

As one of the most important ADAS systems, traditional adaptive cruise control is only suitable for relatively simple working conditions such as expressways, and it is difficult to deal with complex traffic environment and road working conditions. For the problem that the traditional cruise control strategy cannot effectively use the terrain to reduce vehicle fuel consumption under slope conditions and when the vehicle enters a small radius curve such as a ramp, the excessively high turning speed will cause the vehicle to skid. This paper proposes an adaptive cruise control system architecture with adaptability to multiple operating conditions. Simulation and real-vehicle test results show that the adaptive control system architecture and optimisation algorithm proposed in this paper have good applicability to road conditions, and improve the economy of the system under ramp conditions and safety under curve conditions.

About calibration of car-following dynamics of automated and human-driven vehicles: Methodology, guidelines and codes

A comprehensive literature review reveals that there exist lots of ambiguities, confusion and even contradictions in setting a car-following calibration problem. In particular, confusion arises in the selection of measure of performances and goodness-of-fit functions. In this study, a methodology to compare and rank objective functions is thus proposed, which is based on Pareto-efficiency and on indifference curves. The methodology has been applied to all objective functions used in the field literature so far (and to new ones), in a vast set of calibration experiments. The experiments involved two car-following models and two adaptive cruise control (ACC) algorithms, and four different datasets, including both automated and human-driven vehicles trajectories. Since results are consistent among all the calibration experiments, a sound and robust guideline to calibrate car-following dynamics has been proposed. It includes recommendation about what calibration settings should be avoided and what are to be adopted. On the one hand, a general agreement on a sound calibration setting for car-following models is deemed necessary for comparing results from different studies which use different models and datasets. On the other hand, any new car-following model or objective function being developed in the future shall be compared with existing ones in a fair and impartial manner. For these reasons, and to promote and enable transparent and reproducible research, codes and data from this study are shared with the community.

An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC

In this paper, a novel adaptive cruise control (ACC) algorithm based on model predictive control (MPC) and active disturbance rejection control (ADRC) is proposed. This paper uses an MPC algorithm for the upper controller of the ACC system. Through comprehensive considerations, the upper controller will output desired acceleration to the lower controller. In addition, to increase the accuracy of the predictive model in the MPC controller and to address fluctuations in the vehicle’s acceleration, an MPC aided by predictive estimation of acceleration is proposed. Due to the uncertainties of vehicle parameters and the road environment, it is difficult to establish an accurate vehicle dynamic model for the lower-level controller to control the throttle and brake actuators. Therefore, feed-forward control based on a vehicle dynamic model (VDM) and compensatory control based on ADRC is used to enhance the control precision and to suppress the influence of internal or external disturbance. Finally, the proposed optimal design of the ACC system was validated in road tests. The results show that ACC with APE can accurately control the tracking of the host vehicle with less acceleration fluctuation than that of the traditional ACC controller. Moreover, when the mass of the vehicle and the slope of the road is changed, the ACC–APE–ADRC controller is still able to control the vehicle to quickly and accurately track the desired acceleration.

Connected Vehicle-Cooperative Adaptive Cruise Control Algorithm to Divide and Reform Connected Vehicle Platoons at Signalized Intersections to Improve Traffic Throughput and Safety

The cooperative adaptive cruise control (CACC) algorithm is a simple and effective way to form small-headway platoons so that road capacity and traffic throughput can be improved. The CACC algorithm has been broadly discussed in relation to the highway driving environment where frequent stopping and merging are uncommon. This paper proposes that CACC can also benefit urban arterials, using the appropriate algorithm to predict platoon behavior with optimized trajectories to divide and reform platoons before and after signalized intersections, thus maintaining small, safe headways. Connected vehicle (CV) technology is the key to adapting and improving the CACC algorithm, as it enables the signal phasing plan to be sent to a target CACC platoon and allows vehicles to acquire real-time information from other vehicles in the platoon. In this research, a CV-CACC algorithm is proposed consisting of two functions: platoon division and platoon reforming. The new algorithm is also equipped with acceleration as a new control variable instead of speed, so that the platoon is able to accommodate sharp speed changes around intersections, something the baseline CACC is unable to accommodate. In this study, computer simulations have been conducted to test the reliability of the CV-CACC algorithm and compare its performance against the baseline CACC algorithm.

Hazardous Scenario Enhanced Generation for Automated Vehicle Testing Based on Optimization Searching Method

The scenario-based test method is the research hotspot of automated vehicle (AV) validation and verification (V&V), and testing with hazardous scenarios is of important means. An Optimization Searching (OS) method for enhanced generation in hazardous scenarios is proposed in this paper to efficiently explore functional boundary scenarios in a huge logical state space. The method is computationally tractable, and its generated experimental parameters are optimized using past test results. The method includes five essential modules. The Exploration and Exploitation module uses the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-arm bandit</i> method to obtain the greatest sum of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$TTC^{\mathbf {-1}}$ </tex-math></inline-formula> (Time To Collision). The Parameter Moving Probability Determination module uses an analytic hierarchy process to ensure that influential parameters are more likely to move. The Step Size Determination module is built with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Levy-step</i> to find a greater number of hazardous scenarios. The Memory Function module is used to avoid repeat experiments that can reduce computing efficiency. The Result Analysis module creates a hazard parameter space for subsequent tests. We tested an ACC (Adaptive Cruise Control) algorithm with a specified logical scenario in the virtual environment built by PreScan. The results showed that the OS method can effectively discover the dangerous range with the tested ACC algorithm, and its test speed can reach more than five times that of an exhaustive algorithm without prior knowledge.

Congestion-Aware Cooperative Adaptive Cruise Control for Mitigation of Self-Organized Traffic Jams

Previous work has shown that Adaptive Cruise Control (ACC) can improve traffic flow by raising the critical vehicular density at which congestion first appears. However, these works also indicate that traffic with medium-to-high penetration of ACC-equipped vehicles is more susceptible to the formation of self-organized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">phantom</i> traffic jams induced by perturbations in vehicular demographics. In this work, we propose a congestion-aware Cooperative Adaptive Cruise Control (CACC) algorithm as an alternative to address the trade-off between competing goals of raising the critical density, and reducing susceptibility to congestion observed at higher penetration rates of ACC-equipped vehicles. The congestion-aware CACC algorithm is modeled after the General Motor’s car-following models, wherein the driver sensitivity is altered based on the prevailing congestion state (or traffic jam size) downstream of the connected vehicle. The dynamics of the self-organized traffic jam are modeled using a master equation. Results indicate that the congestion-aware CACC algorithm can increase the effective critical density leading to higher traffic flows, while also reducing the susceptibility to perturbations in vehicle demographics in the density range that adversely affects ACC-equipped vehicles.

Defensive Ecological Adaptive Cruise Control Considering Neighboring Vehicles’ Blind-Spot Zones

This paper proposes a defensive ecological adaptive cruise control (DEco-ACC) algorithm that is capable of reducing an ego vehicle’s dwelling time in the blind spot zones (BSZs) of its neighboring vehicles. To this end, a model predictive control is applied in the use of information such as speed, position, and blind spot zones about preceding and neighboring vehicles. The cost function of the DEco-ACC consists of tracking performance, control effort, and dwelling time in BSZs. Specifically, a continuous and one-time differentiable penalty function is introduced to handle the constraints regarding the BSZs. For optimizing and evaluating the performance of the proposed DEco-ACC, real-world traffic data from Next Generation Simulation (NGSIM) are used to analyze and generate car-following scenarios during highway driving. Especially, in consideration of the most probable case that one neighboring vehicle exists at one adjacent lane, a parametric study is conducted to investigate the impact of the weighting factors on the performance of the DEco-ACC. The simulation results from 100 cases demonstrate that on average, the DEco-ACC with optimized weighting factors can reduce the dwelling time in the neighboring vehicles’ BSZs by 46.3% without significant deterioration of fuel consumption (0.04% increase in average fuel consumption) and drivability, as compared to the Eco-ACC, whose primary objective is the minimization of fuel consumption during safe car-following.

String Stability Guaranteed Lane Change Maneuver for Automated Vehicles with Vehicle-to-Vehicle Communication

Abstract This paper considers a specific scenario that is the incorporative operation of lane change with a vehicle platoon. In other words, the lane-change vehicle will insert into the platoon in the main lane and be a member of the platoon. Fast lane-change operation and keeping safe inter-vehicle distance are the significant issues. This work proposes a two-level control strategy to deal with the issue. The high level deals with the trajectory planing for the longitudinal movement of the vehicle platoon and for the lateral movement of the lane-change vehicle, respectively. A distributed adaptive cruise control algorithm, which guarantees string stability of the platoon system, is employed to manage the longitudinal movement. Desired lateral trajectory is generated by solving an optimization problem by using the dynamic programming (DP) method. With the obtained desired longitudinal and lateral trajectories for the lane-change vehicle, the trajectory tracking is realized by the designed low-level control laws that are simple PID controllers. Simulation tests verify the performance of the control system.

Robust control of vehicle multi‐target adaptive cruise based on model prediction

On the issue of low utilisation and acceptance of current adaptive cruise control (ACC), a multi-objective adaptive cruise control (MO-ACC) algorithm is developed in this study. Based on model predictive control theory, comprehensively considering the coordination among various conflicting objectives, the decision of desired longitudinal acceleration is transformed into online quadratic programming (QP) problem. In order to compensate for prediction error caused by modelling mismatch, the robustness of control system is improved by introducing an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non-feasible solution owing to hard constraints during the process of optimisation. Further, under different work conditions, the focusing performance index along with constraint space varies, and therefore different ACC modes are established to meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as slack relaxation. The simulations show that under the combined work conditions of the preceding vehicle, the following vehicle can realise seamless switching among various working modes, and also is able to achieve the good expectation during vehicle following, which will help to enhance the adaptability of the ACC system to the complex road traffic environment.

Safety Analysis of a Modified Cooperative Adaptive Cruise Control Algorithm Accounting for Communication Delay

Cooperative adaptive cruise control (CACC) is a promising technology to improve traffic efficiency and enhance road safety. In this paper, a modified CACC control model considering the communication time delay is proposed, which is used to investigate the longitudinal safety impacts of the communication time delay to the CACC platoon. Then, the communication time delay model is integrated into the CACC model to simulate the realistic information transfer process in the CACC platoon. Then a microscopic CACC platoon simulation is designed and conducted to verify the feasibility and reliability of the modified CACC control algorithm. The obtained results reveal that the modified CACC control algorithm can not only reduce about 96.6% of inter-vehicle spacing error, but also enhance the vehicles’ ability to sense the upstream traffic changes. Furthermore, to quantitatively analyze the longitudinal safety influence of the time delay caused by representative communication systems, sensitivity analysis experiments of headway time were designed and conducted. In the sensitivity analysis, the time exposed time-to-collision (TET) and the time-integrated time-to-collision (TIT) were introduced as the key performance indicators (KPIs) to quantify the rear-end collision risks. Sensitivity analysis results demonstrate that the performance of the CACC platoon is strictly related to the applied wireless communication style. Furthermore, the CACC system supported by the 5th generation (5G) communication system shows great advantages in narrowing the minimal headway time gap and reducing the rear-end collision risks.