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.

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.

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.

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.

Personalized Adaptive Cruise Control Based on Online Driving Style Recognition Technology and Model Predictive Control

2020 ◽  
Vol 69 (11) ◽  
pp. 12482-12496
Author(s):  
Bingzhao Gao ◽  
Kunyang Cai ◽  
Ting Qu ◽  
Yunfeng Hu ◽  
Hong Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Driving Style

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.

Improved Adaptive Cruise Control for Autonomous Vehicles with Consideration of Crash Avoidance

2021 ◽  
Author(s):  
Yu Zhang ◽  
Yunfeng Chu ◽  
Mingming Dong ◽  
Li Gao ◽  
Yechen Qin ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Crash Avoidance

scholarly journals Safety problems in vehicles with adaptive cruise control system

2017 ◽  
Vol 42 (1) ◽  
pp. 389-398 ◽  
Author(s):  
Arun K. Yadav ◽  
Janusz Szpytko
Keyword(s):  
Control System ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Main Concern ◽  
Cruise Control ◽  
Adaptive Cruise Control System ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Driving Stability ◽  
Autonomous Technology

Abstract In today’s world automotive industries are still putting efforts towards more autonomous vehicles (AVs). The main concern of introducing the autonomous technology is safety of driver. According to a survey 90% of accidents happen due to mistake of driver. The adaptive cruise control system (ACC) is a system which combines cruise control with a collision avoidance system. The ACC system is based on laser and radar technologies. This system is capable of controlling the velocity of vehicle automatically to match the velocity of car, bus or truck in front of vehicle. If the lead vehicle gets slow down or accelerate, than ACC system automatically matches that velocity. The proposed paper is focusing on more accurate methods of detecting the preceding vehicle by using a radar and lidar sensors by considering the vehicle side slip and by controlling the distance between two vehicles. By using this approach i.e. logic for calculation of former vehicle distance and controlling the throttle valve of ACC equipped vehicle, an improvement in driving stability was achieved. The own contribution results with fuel efficient driving and with more safer and reliable driving system, but still some improvements are going on to make it more safe and reliable.

scholarly journals Efficient predictive cruise control of autonomous vehicles with improving ride comfort and safety

2020 ◽  
Vol 53 (1-2) ◽  
pp. 18-28
Author(s):  
Defeng He ◽  
Wentao He ◽  
Xiulan Song
Keyword(s):  
Control Problem ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Main Idea ◽  
Driving Safety ◽  
Decision Variable ◽  
Cruise Control ◽  
Constrained Optimal Control ◽  
Control Approach

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.

scholarly journals An Autonomous Vehicle Control Stack

10.29007/r5n9 ◽  
2018 ◽  
Author(s):  
Alena Rodionova ◽  
Matthew O'Kelly ◽  
Houssam Abbas ◽  
Vincent Pacelli ◽  
Rahul Mangharam
Keyword(s):  
Free Space ◽  
Adaptive Cruise Control ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Bench Mark ◽  
High Fidelity ◽  
Cruise Control ◽  
Nonlinear Odes ◽  
Control Subsystem ◽  
Testing Tools

This benchmark presents an implementation of a standard control stack for an Autonomous Vehicle (AV). The control stack is made up of a behavioral planner (providing waypoints for the AV to visit in sequence), a trajectory planner (which computes smooth trajectory that the AV should follow to go between waypoints) and a trajectory tracker (which actuates the AV to make it follow the planned trajectory as closely as possible). The behavioral planner is purposefully simple, while the trajectory planner is a a high-fidelity approximation of a planner that was tested on a real Prius, and the tracker was validated by others in previous work on a real Cadillac SRX. The interest of this benchmark is that it includes all three components, rather than one AV control subsystem (such as only adaptive cruise control or only a lane-keeper), and the planners are significantly more realistic than most existing benchmarks or models. It can be used as a baseline AV system for verification and testing tools, which must be able to handle at least the complexity of this controller. This includes simple choices made by the behavioral planner when the current waypoint cannot be reached, discrete and continuous nonlinear optimizations solved by the trajectory planner, and nonlinear ODEs solved by the trajectory tracker. The bench- mark comes with three road topologies: a free space with obstacles, a curved road, and a roundabout.

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