Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control

2019 ◽  
Author(s):  
Eunjeong Hyeon ◽  
Youngki Kim ◽  
Niket Prakash ◽  
Anna G. Stefanopoulou
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Autonomous Vehicle ◽  
Cruise Control ◽  
Prediction Horizon ◽  
The Future ◽  
V2v Communications ◽  
Urban Conditions ◽  
Lead Vehicle

Abstract In congested urban conditions, the fuel economy of a vehicle can be highly affected by traffic flow and particularly, the immediately preceding (lead) vehicle. Thus, estimating the future trajectories of the lead vehicle is essential to optimize the following vehicle’s maneuvers for its fuel economy. This paper investigates the influence of speed forecasting on the performance of an ecological adaptive cruise control (eco-ACC) strategy for connected autonomous vehicles. The real-time speed predictor proposed in [1] is applied to forecast the future speed profiles of the lead vehicle over a short prediction horizon. Under the assumption that vehicle-to-vehicle (V2V) communications are available, V2V information from multiple lead vehicles is utilized in the prediction process. Eco-ACC is formulated in a model predictive control (MPC) framework to control the connected autonomous vehicle. The influence of the state prediction to the performance of eco-ACC in terms of fuel economy and acceleration is evaluated with different number of connected vehicles.

Modeling and Characterization of Driving Styles for Adaptive Cruise Control in Personalized Autonomous Vehicles

2017 ◽  
Author(s):  
Drew Bolduc ◽  
Longxiang Guo ◽  
Yunyi Jia
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Research Effort ◽  
Autonomous Vehicle ◽  
Individual Preference ◽  
Driver Model ◽  
Cruise Control ◽  
Critical Function ◽  
Driving Style ◽  
Safety And Reliability

For autonomous vehicles to gain widespread customer acceptance, safety and reliability are not nearly enough. Comfort and familiarity of the ride is also of essential importance. Because these are highly subjective factors, autonomous vehicles must be able to adopt personal driving styles to meet individual preference. The adaptive cruise control (ACC) system is a critical function performed by the autonomous vehicle and much research effort has been devoted to the development of a system that acts as a human driver. However, studies which investigate ACC models capable of learning a driving style are limited. In this paper, we propose a method to extract quantifiable parameters which represent a drivers’ driving style and apply these parameters to personalize the longitudinal control of an autonomous vehicle. We then develop a longitudinal driver model that integrates those parameters to enable the ACC system to mimic the driving style of the driver. Finally, the effectiveness of the extraction method and the driver model are obtained through simulation.

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

2016 ◽  
Author(s):  
Niket Prakash ◽  
Gionata Cimini ◽  
Anna G. Stefanopoulou ◽  
Matthew J. Brusstar
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Test Procedures ◽  
Optimal Velocity ◽  
Prediction Horizon ◽  
Allowable Error ◽  
Velocity Constraints ◽  
Lead Vehicle

Constrained optimization control techniques with preview are designed in this paper to derive optimal velocity trajectories in longitudinal vehicle following mode, while ensuring that the gap from the lead vehicle is both safe and short enough to prevent cut-ins from other lanes. The lead vehicle associated with the Federal Test Procedures (FTP) [1] is used as an example of the achieved benefits with such controlled velocity trajectories of the following vehicle. Fuel Consumption (FC) is indirectly minimized by minimizing the accelerations and decelerations as the autonomous vehicle follows the hypothetical lead. Implementing the cost function in offline Dynamic Programming (DP) with full drive cycle preview showed up to a 17% increase in Fuel Economy (FE). Real time implementation with Model Predictive Control (MPC) showed improvements in FE, proportional to the prediction horizon. Specifically, 20s preview MPC was able to match the DP results. A minimum of 1.5s preview of the lead vehicle velocity with velocity tracking of the lead was required to obtain an increase in FE. The optimal velocity trajectory found from these algorithms exceeded the presently allowable error from standard drive cycles for FC testing. However, the trajectory was still safe and acceptable from the perspective of traffic flow. Based on our results, regulators need to consider relaxing the constant velocity error margins around the standard velocity trajectories dictated by the FTP to encourage FE increase in autonomous driving.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Not So Fast: A Brief Plea for Muddling Through the Problems of Autonomous Vehicle Liability

2020 ◽  
Vol 13 (2) ◽  
pp. 189-195
Author(s):  
Adam F. Scales
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Know How ◽  
The Future ◽  
Auto Accidents

AbstractAutonomous Vehicles (AVs) are likely to change a great deal about the practical workings of the liability system for auto accidents. However, we cannot know how just yet. Attempts to anticipate the future and preemptively redesign the liability system around its imagined contours are likely to invite error and frustration. Discretion often being the better part of valor, I suggest we muddle through a bit first.

scholarly journals On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6733
Author(s):  
Min-Joong Kim ◽  
Sung-Hun Yu ◽  
Tong-Hyun Kim ◽  
Joo-Uk Kim ◽  
Young-Min Kim
Keyword(s):  
Autonomous Vehicles ◽  
Focal Length ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Radar Sensors ◽  
Safety And Reliability ◽  
Time Required ◽  
Variable Focus ◽  
Camera Sensors

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

Analysis of a Novel Command Governor-Based Adaptive Cruise Controller for Non-Cooperative Vehicle Following

2019 ◽  
Author(s):  
Ben Groelke ◽  
Christian Earnhardt ◽  
John Borek ◽  
Chris Vermillion
Keyword(s):  
Prediction Models ◽  
Buffer Zone ◽  
Traffic Data ◽  
Cruise Control ◽  
Prediction Horizon ◽  
Trip Time ◽  
Vehicle Velocity ◽  
Wheel Torque ◽  
Different Levels ◽  
Lead Vehicle

Abstract This paper presents a novel adaptive cruise control (ACC) strategy that utilizes a command governor (CG) to enforce vehicle following constraints. The CG formulation relies on knowledge of the maximum possible braking deceleration of the lead vehicle and a tunable assumption regarding the lead vehicle velocity profile (offering different levels of conservatism) to modify wheel torque commands to ensure safe following. In particular, a safe following distance is defined as one in which the ego vehicle can avoid collision with the lead vehicle and maintain a sufficient following distance in the event that the lead vehicle exerts maximum braking deceleration. The CG seeks to adjust the wheel torque command such that the aforementioned constraint is satisfied at every step in a prediction horizon (i.e., at every step, if the lead vehicle exerts maximum braking deceleration, the ego vehicle can brake and remain outside of the aforementioned buffer zone), which requires an estimate of future lead vehicle behavior. In this work, we explore different levels of conservatism with regard to this assumption. Simulations are presented for a heavy-duty truck, using a stochastic lead vehicle model that has been calibrated with actual traffic data. Even for the most conservative lead vehicle prediction models, results show that this CG-based ACC strategy can reduce braking energy expended (used as a surrogate for fuel wasted) by up to 78%, while improving drivability and reducing total trip time.

scholarly journals Time-delay feedback cooperative adaptive cruise control of connected vehicles by heterogeneous channel transmission

2019 ◽  
Vol 52 (5-6) ◽  
pp. 369-378 ◽  
Author(s):  
Xiulan Song ◽  
Xiaoxin Lou ◽  
Limin Meng
Keyword(s):  
Time Delay ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Sufficient Conditions ◽  
Wireless Channel ◽  
Linear Matrix ◽  
Connected Vehicle ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon

In this paper, we consider the cooperative adaptive cruise control problem of connected autonomous vehicles networked by heterogeneous wireless channel transmission. The cooperative adaptive cruise control model with variable input delays is established to describe the varying time-delays induced from vehicular actuators and heterogeneous channel transmission. Then a set of decentralized time-delay feedback cooperative adaptive cruise control controllers is computed in such way that each vehicle evaluates its own adaptive cruise control strategy using only neighborhood information. In order to establish string stability of the connected vehicle platoon with the decentralized controllers, the sufficient conditions are obtained in the form of linear matrix inequalities. The scenarios, consisting of four different cars with three heterogeneous wireless channels, are used to demonstrate the effectiveness of the presented method.

scholarly journals Real-Time Co-optimization of Vehicle Route and Speed Using Generic Algorithm for Improved Fuel Economy

2019 ◽  
Vol 141 (03) ◽  
pp. S08-S15
Author(s):  
Guoming G. Zhu ◽  
Chengsheng Miao
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Combustion Engine ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Hybrid Electric

Making future vehicles intelligent with improved fuel economy and satisfactory emissions are the main drivers for current vehicle research and development. The connected and autonomous vehicles still need years or decades to be widely used in practice. However, some advanced technologies have been developed and deployed for the conventional vehicles to improve the vehicle performance and safety, such as adaptive cruise control (ACC), automatic parking, automatic lane keeping, active safety, super cruise, and so on. On the other hand, the vehicle propulsion system technologies, such as clean and high efficiency combustion, hybrid electric vehicle (HEV), and electric vehicle, are continuously advancing to improve fuel economy with satisfactory emissions for traditional internal combustion engine powered and hybrid electric vehicles or to increase cruise range for electric vehicles.

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