Automatic Cruise Control

How does it work?

Automatic Cruise Control (ACC) uses distance-sensing technology that automatically reduces or accelerates the speed of the vehicle to maintain a constant distance between two vehicles.

ACC systems are an extension of conventional cruise control (CCC) systems that adjust vehicle speed and provide a specific distance to the vehicle ahead by automatically controlling the accelerator and/or brake. A key part of an ACC system is the range sensor, such as a radar, lidar or video camera, which measures the distance and relative speed of the two successive vehicles. In the absence of a vehicle ahead, a vehicle equipped with ACC travels at a speed set by the user controlling the accelerator, much like operating a CCC system.

Adaptive cruise control switch


  • Increases comfort and safety for the driver, is functional for long trips, and in turn optimizes fuel use and engine performance.
  • It allows you to set and manipulate the speed more precisely, for which steering wheel controls are available.
  • Considerably improves attention to the rest of the elements of the road and avoids both visual and physical effort that can occur in the driver.
  • By maintaining safety distances and regulating speed, it improves traffic at an overall level, by generating a smoother drive (i.e., all vehicles traveling at same speed), all the while reducing accidents.
  • This technology is becoming increasingly standard or optional in best-selling vehicles.


  • Depending on where you drive, ACC can generate a type of relaxation on the part of the driver that can easily distract them (i.e., lower concentration on the driving task).
  • Depends on the terrain and the qualities of the road for optimal performance.
  • The effectiveness of automatic braking is not comparable to that of the brake pedal, which is why driver vigilance is required.
  • If drivers can set the following distance, some of them may set it so they are too close to a vehicle in front of them increasing the risk of a rear end collision in case of a rapid necessity of takeover.

Common names

  • Adaptive cruise control
  • Adaptive cruise control with low speed
  • Adaptive cruise control with tail assist
  • Adaptive cruise control with stop
  • Adaptive cruise control with Stop & Go
  • Advanced Intelligent Cruise Control (ASCC)
  • Dynamic cruise control of all speeds
  • Camera-based cruise control
  • Distance assistance
  • Distance pilot
  • Distronic
  • Active Distronic
  • Distronic Plus
  • Cruise control with dynamic radar
  • Cruise control with high-speed dynamic radar
  • Smart Cruise Control (ICC)
  • OEM 1 Adaptive cruise control with Stop & Go
  • OEM 2 Radar cruise control
  • Smart cruise control (stop/start)
  • Traffic-conscious cruise control

Latest Publications on PubMed

Search results for: adaptive cruise control

  • Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis
    by Wayne C W Giang on October 30, 2023 at 10:00 am

    INTRODUCTION: PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few...

  • A Comprehensive Eco-Driving Strategy for CAVs with Microscopic Traffic Simulation Testing Evaluation
    by Ozgenur Kavas-Torris on October 28, 2023 at 10:00 am

    In this paper, a comprehensive deterministic Eco-Driving strategy for Connected and Autonomous Vehicles (CAVs) is presented. In this setup, multiple driving modes calculate speed profiles that are ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High-Level (HL) controller ensures smooth and safe transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure...

  • Car Bumper Effects in ADAS Sensors at Automotive Radar Frequencies
    by Isabel Expósito on October 14, 2023 at 10:00 am

    Radars in the W-band are being integrated into car bumpers for functionalities such as adaptive cruise control, collision avoidance, or lane-keeping. These Advanced Driving Assistance Systems (ADAS) enhance traffic security in coordination with Intelligent Transport Systems (ITS). This paper analyzes the attenuation effect that car bumpers cause on the signals passing through them. Using the free-space transmission technique inside an anechoic chamber, we measured the attenuation caused by car...

  • Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication
    by Razvan-Gabriel Lazar on September 9, 2023 at 10:00 am

    A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today's traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular...

  • Beyond adaptive cruise control and lane centering control: drivers' mental model of and trust in emerging ADAS technologies
    by Chunxi Huang on August 24, 2023 at 10:00 am

    INTRODUCTION: The potential safety benefits of advanced driver assistance systems (ADAS) highly rely on drivers' appropriate mental models of and trust in ADAS. Current research mainly focused on drivers' mental model of adaptive cruise control (ACC) and lane centering control (LCC), but rarely investigated drivers' understanding of emerging driving automation functions beyond ACC and LCC.

  • Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer
    by Jianhua Guo on July 8, 2023 at 10:00 am

    Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load...

  • Data generation for connected and automated vehicle tests using deep learning models
    by Ye Li on June 28, 2023 at 10:00 am

    For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs' tests. Consequently, it is indispensable to increase the richness of accessible trajectory data. In this study, we developed the...

  • Frequency and Quality of Exposure to Adaptive Cruise Control and Impact on Trust, Workload, and Mental Models
    by Ganesh Pai on June 19, 2023 at 10:00 am

    Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS resulting in inadequate mental models. This may result in drivers misusing ADAS, or mistrusting the technologies, especially after encountering edge-case events (situations beyond the capability of an ADAS where the system may malfunction or fail) and may also adversely affect driver workload. Literature suggests mental models could be improved through...

  • Energy-Optimal Adaptive Control Based on Model Predictive Control
    by Yuxi Li on May 13, 2023 at 10:00 am

    Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN). The proposed MPC improves safety and tracking performance while further reducing energy consumption. The final...

  • Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions
    by Zhiru Gu on April 28, 2023 at 10:00 am

    Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning...

  • Impact of lane-changing behavior on traffic emissions of road sections in multi-dimensional mixed traffic flow environment
    by Xinghua Hu on April 14, 2023 at 10:00 am

    This study analyzed the effect of lane-changing behavior on traffic flow emissions and energy consumption of road sections in fuel vehicle-battery electric vehicle (FV-BEV) and human-driven vehicle-cooperative adaptive cruise control (HDV-CACC) multi-dimensional mixed traffic flow environments. Based on the traditional energy consumption model, a multi-dimensional mixed traffic flow energy consumption model was established by considering the BEV and CACC penetration rates. The microscopic...

  • Mixed traffic flow of human-driven vehicles and connected autonomous vehicles: String stability and fundamental diagram
    by Lijing Ma on March 11, 2023 at 11:00 am

    The introduction of connected autonomous vehicles (CAVs) gives rise to mixed traffic flow on the roadway, and the coexistence of human-driven vehicles (HVs) and CAVs may last for several decades. CAVs are expected to improve the efficiency of mixed traffic flow. In this paper, the car-following behavior of HVs is modeled by the intelligent driver model (IDM) based on actual trajectory data. The cooperative adaptive cruise control (CACC) model from the PATH laboratory is adopted for the...

  • An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons
    by Lopamudra Hota on March 11, 2023 at 11:00 am

    Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance...

  • Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions
    by Pingli Ren on February 28, 2023 at 11:00 am

    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,...

  • A CNN-LSTM Car-Following Model Considering Generalization Ability
    by Pinpin Qin on January 21, 2023 at 11:00 am

    To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural driving database and the OpenACC car-following experiment database. Then, we developed a CNN-LSTM car-following model, and the CNN is employed to analyze the potential relationship...