An Adaptive Cruise Control (ACC) system is a driver assistance system that assists a driver to improve driving safety and driving comfort. The design of ACC controller often involves the design of a switching logic that decides where and when to switch between the two modes in order to ameliorate driving comfort, mitigate the chance of a potential collision with the preceding vehicle while reduce long-distance driving load from the driver. In this thesis, a new strategy for designing ACC controller is proposed. The proposed control strategy utilizes Range vs. Range-rate chart to illustrate the relationship between headway distance and velocity difference, and then find out a constant deceleration trajectory on the chart, which the following vehicle is controlled to follow. This control strategy has a shorter elapsed time than existing ones while still maintaining a relatively safe distance during transient process. String stability issue has been addressed by many researchers after the adaptive cruise control (ACC) concept was developed. The main problem is when many vehicles with ACC controller forming a vehicle platoon end to end, how the control algorithm is designed to ensure that the spacing error, which is the deviation of the actual range from the desired headway distance, would not amplify as the number of following vehicles increases downstream along the platoon. In this thesis, string stability issues have been taken into consideration and constraints of parameters of an ACC controller are derived to mitigate steady state error propagation.
A methodology to design the range policy of adaptive cruise control vehicles and its companion servoloop control algorithm is presented in this paper. A nonlinear range policy for improved traffic flow stability and string stability is proposed and its performance is compared against the constant time headway policy, the range policy employed by human drivers, and the Greenshields policy. The proposed range policy is obtained through an optimization procedure with traffic flow and stability constraints. A complementary controller is then designed based on the sliding mode technique. Microscopic simulation results show that stable traffic flow is achieved by the proposed method up to a significantly higher traffic density.
Advances in Intelligent Vehicles presents recent advances in intelligent vehicle technologies that enhance the safety, reliability, and performance of vehicles and vehicular networks and systems. This book provides readers with up-to-date research results and cutting-edge technologies in the area of intelligent vehicles and transportation systems. Topics covered include virtual and staged testing scenarios, collision avoidance, human factors, and modeling techniques. The Series in Intelligent Systems publishes titles that cover state-of-the-art knowledge and the latest advances in research and development in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. - Provides researchers and engineers with up-to-date research results and state-of-the art technologies in the area of intelligent vehicles and transportation systems - Covers hot topics, including driver assistance systems; cooperative vehicle-highway systems; collision avoidance; pedestrian protection; image, radar and lidar signal processing; and V2V and V2I communications
This study resolves the controversy over the stability of constant time-gap policy for highway traffic flow. Previous studies left doubt as to the effectiveness of constant time-gap policies and whether they maintain stability in all traffic conditions. The results of this study prove that the constant time gap policy is in fact stable to a limit. At this limit, depending on the boundary conditions, conditions lose their stability. This study develops alternative ways to maintain the balance between safety and traffic flow for ACC vehicles that does not rely on constant time-gap policies. New spacing policies will create more stability, and therefore safer conditions, and allow for greater traffic capacity.
Over the recent years, a considerable growth in the number of vehicles on the roads has been observed. This increases importance of vehicle safety and of minimization of fuel con- sumption, subsequently prompting manufacturers of cars to equip their products, with more advanced features such as Adaptive Cruise Control (ACC) and Collision Avoidance and Col- lision Warning System (CWS). In this thesis we concentrate on new methods for ACe. This work will include: Design of the simulation models suitable for this application, Investiga- tion and design of suitable hybrid control algorithm by using classical and advanced control algorithm's consisting of the gain scheduling PI and Linear Quadratic (LQ) controllers, De- sign of the Nonlinear Model Predictive Control (NMPC) and the nonlinear Balance-Based Adaptive Control (B-BAC), Real-time implementation and tests of the algorithms by using NI Lab View Starter Kit Robot from National Instruments, Implementation and tests of the models and the controllers in MA TLAB/Simulink®. The applications of the different control methods in the ACC are tested and compared against different traffic scenarios considering both velocity tracking (CC) and distance track- ing (ACC) modes. Judging about the performance of ACC by utilizing the two advanced control methods; B-BAC and NMPC includes trade-offs between tracking-distance and ve- locity and the vehicle acceleration. However, both the B-BAC and the NMPC has demon- strated significantly smoother responses in controlling the throttle and the brake compared to PI control and linear MPC which in turn could improve the vehicle acceleration and fuel ef- ficiency. The methods in order of producing better performance in terms of the values of control errors and their influences on fuel saving; NMPC, B-BAC, linear MPC and PI con- trol. Improvement of fuel efficiency is investigated in this thesis through two approaches; first, by calculating the optimal control actions corresponding to the throttle and the brake signals through utilising the advanced control methods, second, by reducing the engine speed to idle speed during coast phase of the vehicle which causes the engine friction to be re- duced. The engine speed can be reduced through transition between locked and unlocked states of the torque convertor. Possibility of achieving fuel efficiency through coasting in the vehicle has been investigated in the simulation and it has been demonstrated that longer coasting duration could be achieved i.e. more distance can be covered, and the fuel effi- ciency could be improved.
Traffic congestion is an important reason for driver frustration which in turn is the main cause of human errors and accidents. Statistics reports have shown that over 90% of accidents are caused by human errors. Therefore, it is vital to improve vehicle controls to ensure adequate safety measures in order to decrease the number of accidents or to reduce the impact of accidents. An application of mathematical control techniques to the longitudinal dynamics of a vehicle equipped with an adaptive cruise control (ACC) system is presented. This study is carried out for the detailed understanding of a complex ACC vehicle model under critical transitional manoeuvres (TMs) in order to establish safe inter-vehicle distance with zero range-rate (relative velocity) behind a preceding vehicle. TMs are performed under the influence of internal complexities from vehicle dynamics and within constrained operation boundaries. The constrained boundaries refer to the control input, states, and collision avoidance constraints. The ACC vehicle is based on a nonlinear longitudinal model that includes vehicle inertial and powertrain dynamics. The overall system modelling includes: complex vehicle models, engine maps construction, first-order vehicle modelling, controllers modelling (upper-level and lower-level controllers for ACC vehicles). The upper-level controller computes the desired acceleration commands for the lower-lever controller which then provides the throttle/brake commands for the complex vehicle model. An important aspect of this study is to compare four control strategies: proportional-integral-derivative; sliding mode; constant-time-gap; and, model predictive control for the upper-level controller analysis using a first-order ACC vehicle model. The first-order model represents the lags in the vehicle actuators and sensor signal processing and it does not consider the dynamic effects of the vehicle's sub-models. Furthermore, parameter analyses on the complex ACC vehicle for controller and vehicle parameters have been conducted. The comparison analysis of the four control strategies shows that model predictive control (MPC) is the most appropriate control strategy for upper-level control because it solves the optimal control problem on-line, rather than off-line, for the current states of the system using the prediction model, at the same time being able to take into account operation constraints. The analysis shows that the complex ACC vehicle can successfully execute TMs, tracking closely the desired acceleration and obeying the constraints, whereas the constraints are only applied in the MPC controller formulation. It is found that a higher length of the prediction horizon should be used for a closed acceleration tracking. The effect of engine and transmission dynamics on the MPC controller and ACC vehicle performance during the gear shifting is studied. A sensitivity analysis for MPC controller and vehicle parameters indicates that a length of the control horizon that is too high can seriously disturb the vehicle behaviour, and this disturbance can be only removed if a higher value of control input cost weighting is used. Furthermore, the analysis indicates that a mass within the range of 1400-2000 kg is suitable for the considered ACC vehicle. It is recommended that a variable headway time should be used for the spacing control between the two vehicles. It is found that the vehicle response is highly sensitive to the control input cost weighting; a lower value (less than one) can lead to a highly unstable vehicle response. It is recommended that the lower-level controller must take into account the road gradient information because the complex ACC vehicle is unable to achieve the control objectives while following on a slope. Based on the results, it is concluded that a first-order ACC vehicle model can be used for the controller design, but it is not sufficient to capture the complex vehicle dynamic response. Therefore, a complex vehicle model should be of use for the detailed ACC vehicle analysis. In this research study the first-order ACC vehicle model is used for the complex vehicle validation, whereas the complex ACC vehicle model can be used for the experimental validation in future work.
Modern automobiles are equipped with various driver assistance functions which enhance safety and relieve driver fatigue. With the recent development of sensor technology, the Adaptive Cruise Control (ACC) system has been put into practice. This thesis investigates several aspects for the ACC system including (1) smooth reaction of the host vehicle to the cutting in and out of lead vehicles, (2) real-time optimal profile generation for stop-and-go motions, (3) optimal feedback controller design, and (4) extension to Cooperative Adaptive Cruise Control (CACC) systems. The ACC system should maintain an appropriate relative distance to the lead vehicle and should also maintain the desired speed set by the driver if there is no lead vehicle or if the speed of the lead vehicle is faster than the desired speed. Also, it should react smoothly when the lead vehicle cuts out or if a new lead vehicle cuts in from a side lane. This thesis introduces the virtual lead vehicle scheme to prevent the switching between the distance control and the speed control. By controlling the motion of the virtual lead vehicle to be smooth, the scheme could provide smooth reaction of the host vehicle to the cutting in and out of lead vehicles. Linear Quadratic (LQ) optimal control scheme is utilized to find the control gains for the virtual lead vehicle and the host vehicle. Variable weights are utilized in LQ for the virtual lead vehicle. With the variable weights, the motion of the virtual lead vehicle is controlled to be smooth when there is no safety threat while ensuring that the virtual lead vehicel is still responsive and fast when a dangerous situation occurs. ACC with Stop-and-Go and the Cooperative Adaptive Cruise Control (CACC) system are extensions of the conventional ACC system. Stop-and-Go system is targeted to be used in urban driving situation where the lead vehicle can stop completely. In that case, the Stop-and-Go system should have a capability to stop the host vehicle completely. The constant time-headway policy used to find the appropriate relative distance causes undesirable motion for a complete stop. In this thesis a sliding controller is utilized to control the complete stopping motion. To find the optimal stopping trajectory, a constrained Quadratic Programming (QP) problem is solved. A constrained QP is also used to find the optimal velocity profile when the stopped vehicle is to resume motion. Multi-resolution formulations and the Lemke algorithm are utilized to find the optimal trajectories in real time. The CACC system utilizes wireless communication so that the vehicles in the network can share information with other vehicles. In this thesis, a centralized controller is designed by LQ optimal control scheme and potential benefits and problems are addressed. A Kalman filter with variable measurement noise covariance is introduced to compensate the lost data through the wireless network associated with the CACC system. The proposed control schemes have been verified through simulations.
