Advanced Adaptive Cruise Control Research Articles

Advanced adaptive cruise control and vehicle to vehicle communication using LiDAR

The ACC system is a type of driving assistance that helps reduce the burden on a driver. It operates by controlling the vehicle's acceleration and deceleration to maintain a certain speed or avoid a collision. In order to improve the accuracy of its operation, this paper proposes a method that uses radar to detect the presence of the preceding vehicle. It also takes into account the vehicle sideslip angle and the distance between the two vehicles. The proposed method can help improve the driving stability. In addition, we are currently using the vehicle-to-vehicle communication technology in our system. This type of inter-vehicle communication is not widely used yet and is a new paradigm in the field of communication. One of the main advantages of this type of communication is that it doesn't rely on third-party networks such as cellular networks. Despite the various advantages of this technology, it still has a long way to go before it can be fully deployed. The increasing number of operators who are interested in this technology has led to the development of more effective and efficient V2V solutions. This paper aims to provide a comprehensive analysis of the various challenges and benefits of this type of communication. The paper also focuses on the various security issues that are associated with this technology. One of these is the potential interference. This issue can prevent the system from operating efficiently. In order to address this issue, the paper suggests an improved architectural solution that can help prevent this type of interference. The proposed work also proposes a standardized space for the various components of the V2V system. This will allow them to easily integrate into the system. Vehicle-to-Vehicle communication is a type of wireless communication that is designed to allow cars to talk to one another.

Toward Learning Human-Like, Safe and Comfortable Car-Following Policies With a Novel Deep Reinforcement Learning Approach

In this paper, we present an advanced adaptive cruise control (ACC) concept powered by Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car-following policies. Unlike the current trend in developing DRL-based ACC systems, we propose defining the action space of the DRL agent with discrete actions rather than continuous ones, since human drivers never set the throttle/brake pedal level to be actuated, but rather the required change of the current pedal levels. Through this human-like throttle-brake manipulation representation, we also define explicit actions for holding (keeping the last action) and coasting (no action), which are usually omitted as actions in ACC systems. Moreover, based on the investigation of a real-world driving dataset, we cast a novel reward function that is easy to interpret and personalized. The proposed reward enforces the agent to learn stable and safe actions, while also encouraging the holding and coasting actions, just like a human driver would. The proposed discrete action DRL agent is trained with action masking, and the reward terms are completely derived from the real-world dataset collected from a human driver. We present exhaustive comparative results to show the advantages of the proposed DRL approach in both simulation and scenarios extracted from real-world driving. We clearly show that the proposed policy imitates human driving significantly better and handles complex driving situations, such as cut-ins and cut-outs, implicitly, in comparison with a DRL agent trained with a widely-used reward function proposed for ACC, a model predictive control structure, and traditional car-following approaches.

On multi-rate prediction governor for CACC system with sensor errors and communication delays

Among automated driving technologies, ACC (Adaptive Cruise Control), which controls the distance between vehicles and relative speed, is becoming popular. CACC (Cooperative ACC), which is an advanced ACC system, uses V2V communication and its sensors to control the distance between vehicles. Sensor errors and communication delays have a significant impact on control performance of CACC. In this study, error correction of CACC is performed using accurate information with delay. A noise shaping feedforward controller called a predictive governor is used for this purpose. When applying CACC system to the predictive governor, we cannot design the stable prediction governor due to the relative order or zero characteristics of the CACC system. Therefore, this paper proposes multi-rate type prediction governor that can be applied to the CACC system. The results show that the use of multi-rate enables the application of the prediction governor to CACC systems, and more accurate temporary stopping is achieved with sensor noise and delayed communication.

Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers.

Using advanced adaptive cruise control systems to reduce congestion at sags: An evaluation based on microscopic traffic simulation

Sags are roadway sections along which the gradient increases gradually in the direction of traffic. Sags are generally bottlenecks in freeway networks. Previous research suggests that traffic management measures using advanced Adaptive Cruise Control (ACC) systems could reduce congestion on freeways, but little is known about their potential effectiveness at sags. This article evaluates the effectiveness of a basic ACC system (B-ACC) and two advanced ACC systems – Traffic State-Adaptive ACC (TSA-ACC) and Cooperative ACC (C-ACC) – in mitigating congestion at sags. TSA-ACC adapts the ACC parameters to the macroscopic traffic state estimated by the vehicle itself. C-ACC uses information of other vehicles in the surroundings to adjust its accelerations. Results are obtained using microscopic traffic simulations with different penetration rates. They show that, under high-demand conditions, congestion decreases with increasing percentage of vehicles equipped with B-ACC. With high penetration rates (75% and above), traffic no longer becomes congested at the sag. Moreover, the results show that TSA-ACC and C-ACC reduce congestion more than B-ACC, mainly because they increase the queue discharge capacity of the sag. The two advanced ACC systems prevent the formation of congestion at the sag at lower penetration rates than B-ACC. TSA-ACC is the most effective system. C-ACC is only more effective than B-ACC in scenarios with 20% penetration rate or higher; below that, connectivity between equipped vehicles is too low. Our findings show the potential of using advanced ACC systems to mitigate congestion at sags and indicate some challenges of this traffic management approach.

Relative velocity difference model for the car-following theory

To explore and evaluate the impacts of relative velocity difference (RVD) with memory on the dynamic characteristics and fuel economy of traffic flow in the intelligent transportation environment, we first analyze the linkage between RVD with different-step memory and the following car’s behaviors with the measured car-following (CF) data in cities by using the gray correlation analysis method and then present a RVD model based on the previous CF models in the literatures and calibrate it. Finally, we conduct several numerical simulations in the adaptive cruise control (ACC) strategy to explore how RVD with memory affects car’s velocity fluctuation and fuel consumptions, and find that the RVD model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, and that considering RVD with memory in modeling CF behaviors and designing the advanced ACC strategy can improve the stability and fuel economy of traffic flow.

Integration of auto‐steering with adaptive cruise control for improved cornering behaviour

Several works have proposed longitudinal control strategies enabling a vehicle to operate adaptive cruise control and collision avoidance functions. However, no integration with lateral control has been proposed in the current state of the art, which motivates the developments of this work. This study presents an integrated control strategy for adaptive cruise control with auto-steering for highway driving. An appropriate logic-based control strategy is used to create synergies and safe interaction between longitudinal and lateral controllers to obtain both lateral stability and advanced adaptive cruise control functionalities. In particular, an index is proposed to evaluate lateral motion of the vehicle based on previously published experimental studies on human driving. In order to handle unstable lateral motion of the vehicle, the desired acceleration is determined based on physical limitation in braking with cornering situations. Simulation results show that the proposed integrated controller satisfies the performance in terms of autonomous driving, path tracking and collision avoidance for various driving situations.

Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system

ABSTRACTObjective: Adaptive cruise control (ACC) has been investigated recently to explore ways to increase traffic capacity, stabilize traffic flow, and improve traffic safety. However, researchers seldom have studied the integration of ACC and roadside control methods such as the variable speed limit (VSL) to improve safety. The primary objective of this study was to develop an infrastructure-to-vehicle (I2V) integrated system that incorporated both ACC and VSL to reduce rear-end collision risks on freeways.Methods: The intelligent driver model was firstly modified to simulate ACC behavior and then the VSL strategy used in this article was introduced. Next, the I2V system was proposed to integrate the 2 advanced techniques, ACC and VSL. Four scenarios of no control, VSL only, ACC only, and the I2V system were tested in simulation experiments. Time exposed time to collision (TET) and time integrated time to collision (TIT), 2 surrogate safety measures derived from time to collision (TTC), were used to evaluate safety issues associated with rear-end collisions. The total travel times of each scenario were also compared.Results: The simulation results indicated that both the VSL-only and ACC-only methods had a positive impact on reducing the TET and TIT values (reduced by 53.0 and 58.6% and 59.0 and 65.3%, respectively). The I2V system combined the advantages of both ACC and VSL to achieve the most safety benefits (reduced by 71.5 and 77.3%, respectively). Sensitivity analysis of the TTC threshold also showed that the I2V system obtained the largest safety benefits with all of the TTC threshold values. The impact of different market penetration rates of ACC vehicles in I2V system indicated that safety benefits increase with an increase in ACC proportions.Conclusions: Compared to VSL-only and ACC-only scenarios, this integrated I2V system is more effective in reducing rear-end collision risks. The findings of this study provide useful information for traffic agencies to implement novel techniques to improve safety on freeways.

The effects of velocity difference changes with memory on the dynamics characteristics and fuel economy of traffic flow

Abstract To evaluate the effects of velocity difference changes with memory in the intelligent transportation environment on the dynamics and fuel consumptions of traffic flow, we first investigate the linkage between velocity difference changes with memory and car-following behaviors with the measured data in cities, and then propose an improved cooperative car-following model considering multiple velocity difference changes with memory in the cooperative adaptive cruise control strategy, finally carry out several numerical simulations under the periodic boundary condition and at signalized intersections to explore how velocity difference changes with memory affect car’s velocity, velocity fluctuation, acceleration and fuel consumptions in the intelligent transportation environment. The results show that velocity difference changes with memory have obvious effects on car-following behaviors, that the improved cooperative car-following model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, that the stability and fuel economy of traffic flow simulated by the improved car-following model with velocity difference changes with memory is obviously superior to those without velocity difference changes, and that taking velocity difference changes with memory into account in designing the advanced adaptive cruise control strategy can significantly improve the stability and fuel economy of traffic flow.

An improved car-following model considering relative velocity fluctuation

To explore and evaluate the impacts of relative velocity fluctuation on the dynamic characteristics and fuel consumptions of traffic flow, we present an improved car-following model considering relative velocity fluctuation based on the full velocity difference model, then we carry out several numerical simulations to determine the optimal time window length and to explore how relative velocity fluctuation affects cars’ velocity and its fluctuation as well as fuel consumptions. It can be found that the improved car-following model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, and that taking relative velocity fluctuation into account in designing the advanced adaptive cruise control strategy can improve the traffic flow stability and reduce fuel consumptions.

Cascade Extended Kalman Filter 기반의 차량동특성 및 도로종단경사 추정

Vehicle dynamic states used in various advanced driving safety systems are influenced by road geometry. Among the road geometry parameters, the vehicle pitch angle influenced by road slope and acceleration-deceleration is essential parameters used in pose estimation, advanced adaptive cruise control and others on sag road. Although the road slope data is essential data, the method measuring the data is not commercialized. The digital map including the road geometry data and high-precision DGPS system such as DGPS(Differential Global Positioning System) based RTK(Real-Time Kinematics) are used unusually. In this paper, low-cost cascade extended Kalman filter(CEKF) based vehicle pitch angle estimation method is proposed. It use two cascade EKFs. The EKFs use several measured vehicle states, for example yaw rate, longitudinal acceleration, lateral acceleration and wheel speed of the rear tiers and 3 D.O.F(Degree Of Freedom) vehicle dynamics model. The performance of proposed estimation algorithm is evaluated by simulation based on Carsim dynamics tool and T-car based experiment.

Modeling and Deployment of Model-Based Decentralized Embedded Diagnosis inside Vehicles: Application to Smart Distance Keeping Function

The deployment of a fault diagnosis strategy in the Smart Distance Keeping (SDK) system with a decentralized architecture is presented. The SDK system is an advanced Adaptive Cruise Control (ACC) system implemented in a Renault-Volvo Trucks vehicle to increase safety by overcoming some ACC limitations. One of the main differences between this new system and the classical ACC is the choice of the safe distance. This latter is the distance between the vehicle equipped with the ACC or the SDK system and the obstacle-in-front (which may be another vehicle). It is supposed fixed in the case of the ACC, while variable in the case of the SDK. The variation of this distance depends essentially on the relative velocity between the vehicle and the obstacle-in-front. The main goal of this work is to analyze measurements, issued from the SDK elements, in order to detect, to localize, and to identify some faults that may occur. Our main contribution is the proposition of a decentralized approach permitting to carry out an on-line diagnosis without computing the global model and to achieve most of the work locally avoiding huge extra diagnostic information traffic between components. After a detailed description of the SDK system, this paper explains the model-based decentralized solution and its application to the embedded diagnosis of the SDK system inside Renault-Volvo Truck with five control units connected via a CAN-bus using “Hardware in the Loop” (HIL) technique. We also discuss the constraints that must be fulfilled.

Faster Clearing of Congestion on Expressways using Advanced Adaptive Cruise Control System

If the current adaptive cruise control systems(hereafter referred to as ACC) is fitted to vehicles running on expressways, the occurrence of congestion will be relieved by maintaining the appropriate following distance. However, it does not have the function of appropriately controlling starting and following of a vehicle in congestion, so it is considered that once congestion has occurred, ACC is not useful for faster clearing of the congestion. Therefore, in this research an advanced ACC is proposed in which the function of starting and following is added to conventional ACC, and an estimate of the extent to which congestion would be relieved by installing this to vehicles was made based on current traffic data. The results indicate that, for example, when 25% of vehicles are installed with advanced ACC, the congestion volume would be relieved by about 40%. This shows that faster clearing of congestion can be achieved by increasing the use of advanced ACC.

Faster Clearing of Congestion on Expressways using Advanced Adaptive Cruise Control System

Faster Clearing of Congestion on Expressways using Advanced Adaptive Cruise Control System