scholarly journals Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 749-763
Author(s):  
Srikanth Kolachalama ◽  
Hafiz Malik
Keyword(s):  
Predictive Model ◽  
Adaptive Cruise Control ◽  
Performance Comparison ◽  
Operating Conditions ◽  
Cruise Control ◽  
Future Time ◽  
Driver Behaviour ◽  
Body Parameter ◽  
Control Set ◽  
Engine Operating Point

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP.

scholarly journals Predictive Model of ACC Speed to Enhance Engine Operating Conditions

2021 ◽  
Author(s):  
Srikanth Kolachalama ◽  
Hafiz Malik
Keyword(s):  
Predictive Model ◽  
Real Time ◽  
Adaptive Cruise Control ◽  
Engine Performance ◽  
Performance Comparison ◽  
Operating Conditions ◽  
Cruise Control ◽  
Future Time ◽  
Control Set ◽  
Engine Operating Point

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

scholarly journals Driver behaviour with adaptive cruise control

Ergonomics ◽  
2005 ◽  
Vol 48 (10) ◽  
pp. 1294-1313 ◽  
Author(s):  
Neville A. Stanton ◽  
Mark S. Young
Keyword(s):  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Driver Behaviour

A Complete Target Selection Method for ACC System Based on Statistics and Classification of Vehicle Trajectories

2014 ◽  
Vol 533 ◽  
pp. 316-320 ◽  
Author(s):  
Jian Wu ◽  
Shi Feng Geng ◽  
Yang Zhao
Keyword(s):  
Adaptive Cruise Control ◽  
Target Selection ◽  
Operating Conditions ◽  
Lane Change ◽  
Cruise Control ◽  
Driving Behaviors ◽  
Vehicle Trajectories ◽  
Path Prediction ◽  
Path Curvature

The uncertainty of driving behaviors of all cars and trajectories variation of preceding cars with changing path curvature make it hard for traditional radar-based Adaptive Cruise Control (ACC) system to choose its valid target, which is caused by the deficient judgment about the preceding curves and the behaviors of preceding cars. Through statistics and classification of the trajectories that host and preceding objects generate, the proposed method could differentiate the operating conditions of each car, either in straight lane, on curve or in lane-change, thus front path prediction and host vehicles future lane estimation can be well fulfilled. From radar and host cars information a coordinate that changes under several criteria can be established, based on which the trajectories of all cars can be classified and analyzed. This complete method can find the valid target for ACC system and enable the system to overcome some typical defects of traditional ACC, such as the confusion between lane-change and curve-enter of preceding cars, and also the speed of preceding cars can be modified as soon as they enter curves. HIL test have been conducted to validate the method.

scholarly journals Adaptive car control system based on a predictive model

2021 ◽  
Vol 1208 (1) ◽  
pp. 012040
Author(s):  
Amel Toroman ◽  
Samir Vojić
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Predictive Model ◽  
Rise Time ◽  
Pid Controller ◽  
Control Algorithm ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Traffic Incidents ◽  
Cruise Control System

Abstract An adaptive control is a control, which by pre-setting the parameters of the controller, enables the control of processes whose parameters are time-varying or are initially uncertain. The possibilities and benefits of adaptive control are versatile and can be best demonstrated by applying the system while driving a car, or maintaining the optimal speed and distance between cars, which is shown in this paper. As the car’s weight decreases while driving due to fuel consumption, the control algorithm has to be adapted to the changed driving conditions. Accordingly, an adaptive control system using the Matlab software package, and an adaptive cruise control system (ACC) was created in this paper, which is based on a predictive model. After evaluating the developed model of adaptive car motion control, the output parameters such as speed, acceleration, and distance between the two vehicles were analyzed. In this paper a PID controller is used to reduce oscillations in the system. First, the P controller was used to reduce the rise time of the significant values, then the PI controller improved the rise time, and finally the PID controller achieved overshoot reduction performance without affecting the dynamic response system. The obtained results confirm the justified expectations for the possibility of adaptive car control utilization as one of the possible solutions to the increasing traffic incidents, as well as a measure to improve the reduction of these incidents.

scholarly journals Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

2020 ◽  
Vol 16 (3) ◽  
pp. 776-806
Author(s):  
Silvia F. Varotto ◽  
Haneen Farah ◽  
Klaus Bogenberger ◽  
Bart van Arem ◽  
Serge P. Hoogendoorn
Keyword(s):  
Adaptive Cruise Control ◽  
Full Range ◽  
Cruise Control ◽  
Driver Behaviour ◽  
Road Study

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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