Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

2021 ◽  
Vol 144 (1) ◽  
Author(s):  
Eunjeong Hyeon ◽  
Youngki Kim ◽  
Tulga Ersal ◽  
Anna Stefanopoulou
Keyword(s):  
Real World ◽  
Adaptive Cruise Control ◽  
Polynomial Regression ◽  
Weighting Function ◽  
Prediction Method ◽  
Least Square ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Weighting Method ◽  
Discount Factors

Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

scholarly journals The Effects of Vehicle Automation on Driver Engagement: The Case of Adaptive Cruise Control and Mind Wandering

2020 ◽  
pp. 001872082097485
Author(s):  
Starla M. Weaver ◽  
Stephanie M. Roldan ◽  
Tracy B. Gonzalez ◽  
Stacy A. Balk ◽  
Brian H. Philips
Keyword(s):  
Adaptive Cruise Control ◽  
Field Research ◽  
Physiological Arousal ◽  
Mind Wandering ◽  
Driving Performance ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Positive Effects ◽  
And Gender ◽  
Vehicle Automation

Objective This field study examined the effects of adaptive cruise control (ACC) on mind wandering prevalence. Background ACC relieves the driver of the need to regulate vehicle speed and following distance, which may result in safety benefits. However, if ACC reduces the amount of attentional resources drivers must devote to driving, then drivers who use ACC may experience increased periods of mind wandering, which could reduce safety. Methods Participants drove a prescribed route on a public road twice, once using ACC and once driving manually. Mind wandering rates were assessed throughout the drive using auditory probes, which occurred at random intervals and required the participant to indicate whether or not they were mind wandering. Measures of physiological arousal and driving performance were also recorded. Results No evidence of increased mind wandering was found when drivers used ACC. In fact, female drivers reported reduced rates of mind wandering when driving with ACC relative to manual driving. Driving with ACC also tended to be associated with increased physiological arousal and improved driving behavior. Conclusion Use of ACC did not encourage increased mind wandering or negatively affect driving performance. In fact, the results indicate that ACC may have positive effects on driver safety among drivers who have limited experience with the technology. Application Driver characteristics, such as level of experience with in-vehicle technology and gender, should be considered when investigating driver engagement during ACC use. Field research on vehicle automation may provide valuable insights over and above studies conducted in driving simulators.

scholarly journals Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2433
Author(s):  
Hao Chen ◽  
Hesham A. Rakha
Keyword(s):  
Energy Consumption ◽  
Adaptive Cruise Control ◽  
Combustion Engine ◽  
Signalized Intersections ◽  
Simulation Software ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Optimal Speed ◽  
Cooperative Adaptive Cruise Control ◽  
The Impact

This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.

Safe Adaptive Cruise Control with Control Barrier Function and Smith Predictor

2020 ◽  
Author(s):  
Caio I. G. Chinelato ◽  
Bruno A. Angélico
Keyword(s):  
Numerical Simulations ◽  
Barrier Function ◽  
Adaptive Cruise Control ◽  
Smith Predictor ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Control Lyapunov Function ◽  
And Control ◽  
Safe Set ◽  
Initial Results

This work presents the development of Adaptive Cruise Control (ACC) applied to a vehicle. The ACC tracks a predefined controlled vehicle cruise speed, however when a leading vehicle with lower speed is encountered, the ACC must adapt the controlled vehicle speed to maintain a safe distance between the vehicles. The control strategy applied combines Control Lyapunov Function (CLF), related to performance/stability objectives and Control Barrier Function (CBF), related to safety conditions represented by a safe set. CLF and CBF are integrated with Quadratic Programming (QP) and a relaxation is used to make performance/stability objectives as a soft constraint and safety conditions as a hard constraint. The system model is based on a vehicle available at EPUSP and presents an input time-delay, that can degrade performance and stability. The input delay is compensated with a Smith Predictor. The initial results were obtained through numerical simulations and, in the future, the scheme will be implemented in the vehicle. The numerical simulations indicate that the proposed controller respect the performance/stability objectives and the safety conditions.

Mobility and Energy Consumption Impacts of Cooperative Adaptive Cruise Control Vehicle Strings on Freeway Corridors

2020 ◽  
Vol 2674 (9) ◽  
pp. 111-123
Author(s):  
Hao Liu ◽  
Xiao-Yun Lu ◽  
Steven E. Shladover
Keyword(s):  
Energy Consumption ◽  
Fuel Economy ◽  
Adaptive Cruise Control ◽  
Baseline Scenario ◽  
Vehicle Speed ◽  
Market Penetration ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control ◽  
Freeway Corridors ◽  
The Impact

Cooperative adaptive cruise control (CACC) vehicle string operations have the potential to improve significantly the mobility and energy consumption performance of congested freeway corridors. This study examines the impact of CACC string operations on vehicle speed and fuel economy on the 13-mi SR-99 corridor, near Sacramento, CA. It extends the existing body of knowledge by performing a multi-scenario simulation analysis of the freeway corridor. A simulation study evaluated the performance of the corridor under various CACC market penetration scenarios and traffic demand inputs. The CACC string operation was also analyzed when vehicle awareness device (VAD) and CACC managed lane (ML) strategies were implemented. The case study revealed that the average vehicle speed increased by 70% when the CACC market penetration increased from 0% to 100%. The highest average fuel economy, expressed in miles per gallon (mpg), was achieved under the 50% CACC scenario where mpg was 27. This was 10% higher than the baseline scenario. However, when the CACC market penetration was 50% or higher, the vehicle fuel efficiency only had minor increases. When CACC market penetration reached 100%, the corridor allowed 30% more traffic to enter the network without experiencing reduced average speed. Results also indicate that the VAD strategy increased the speed by 8% when the CACC market penetration was 20% or 40%, while there was a minor decrease in mpg. The ML strategy decreased the corridor performance when implemented alone.

scholarly journals The energy impact of adaptive cruise control in real-world highway multiple-car-following scenarios

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Yinglong He ◽  
Michail Makridis ◽  
Georgios Fontaras ◽  
Konstantinos Mattas ◽  
Hongming Xu ◽  
...  
Keyword(s):  
Real World ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Car Following ◽  
Energy Impact

scholarly journals Incorporating Driver Relaxation into Factory Adaptive Cruise Control and its Implications on Traffic Operation

2021 ◽  
Author(s):  
Hao Zhou ◽  
Jorge Laval
Keyword(s):  
Adaptive Cruise Control ◽  
Smooth Transition ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Lane Changing ◽  
Speed Reduction ◽  
Time Headway ◽  
Traffic Operation ◽  
The Impact ◽  
Lateral Propagation

Current adaptive cruise control (ACC) systems adopt fixed desired time headway, which leads to an abrupt speed reduction after being cut-in by a lane changer in front or when changing lanes too close to the new leader. In contrast, human drivers behave differently and feature a variable spacing within 20 or 30 seconds right after a cut-in or lane change. Motivated by the smooth transition found in driver relaxation, the paper aims to incorporate relaxation into ACC systems. Based on the open-source ACC platform, Openpilot, Comma.ai, the paper proposes a feasible relaxation model compatible with current factory ACCs, which has also been tested using a market car with stock ACC hardware. The study further investigates the impact of relaxation ACC on traffic operation. Numerical simulation suggests that incorporating relaxation into ACC can help: i) reduce the magnitude of speed perturbations in both cut-in vehicles and followers; ii) stabilize the lane-changing traffic by reducing the speed variance and prevent the lateral propagation of congestion, and iii) increase the average vehicle speed and capacity in merging traffic.

scholarly journals Lane-change prediction method for adaptive cruise control system with hidden Markov model

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401880293 ◽  
Author(s):  
Wei Yuan ◽  
Zhen Li ◽  
Chang Wang
Keyword(s):  
Control System ◽  
Markov Model ◽  
Adaptive Cruise Control ◽  
Time Window ◽  
Hidden Markov ◽  
Prediction Method ◽  
Window Length ◽  
Cruise Control ◽  
Lane Changing ◽  
Cruise Control System

A test platform with a millimeter-wave radar sensor, lane-line sensor, gyroscope, and controller area network was established to improve safety in using adaptive cruise control systems in vehicles. The motion-state characterization data of the host vehicle and surrounding vehicles in a real traffic environment were captured. The prediction method for the lane-changing maneuver of the vehicle ahead was developed using a hidden Markov model based on the distance between the host vehicle and the front vehicle, as well as the lateral and longitudinal velocities of the vehicle in front. The adaptive cruise control system control algorithm for assessing the target vehicle was optimized. The model was tested, and its predictions were compared with measured data. Result shows that the lane-changing and lane-keeping behaviors of the vehicle ahead can be predicted efficiently and accurately by the model. The maximum prediction accuracy rate for straight roads was 97% with the time window length of 4.5 s, whereas that for curved roads was 96% with the time window length of 3.5 s.

Potential field–based hierarchical adaptive cruise control for semi-autonomous electric vehicle

2018 ◽  
Vol 233 (10) ◽  
pp. 2479-2491 ◽  
Author(s):  
Yue Ren ◽  
Ling Zheng ◽  
Wei Yang ◽  
Yinong Li
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Potential Field ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Tracking Error ◽  
Driving Safety ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Collision Risk

Adaptive cruise control, as a driver assistant system for vehicles, can adjust the vehicle speed to keep the appropriate distance from other vehicles, which highly increases the driving safety and driver’s comfort. This paper presents hierarchical adaptive cruise control system that could balance the driver’s expectation, collision risk, and ride comfort. In the adaptive cruise control structure, there are two controllers to achieve the function. The one is the upper controller which is established based on the model predictive control theory and used to calculate the desirable longitudinal acceleration. The collision risk is described by the Gaussian distribution. A quadratic cost function for model predictive control is formulated based on the potential field method through the contradictions between the tracking error, collision risk, and the longitudinal ride comfort. The other one is the lower optimal torque vectoring controller which is constructed based on the vehicle longitudinal dynamics. And it can generate the desired acceleration considering the anti-wheel slip limitations. Several simulations under different road conditions demonstrate that the proposed adaptive cruise control has significant performance on balancing the tracking ability, collision avoidance, ride comfort, and adhesion utilization. It also maintains vehicle stability for the complex road conditions.

scholarly journals Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1139 ◽  
Author(s):  
Bo Hu ◽  
Jiaxi Li ◽  
Jie Yang ◽  
Haitao Bai ◽  
Shuang Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Cruise Control ◽  
Computational Time ◽  
Small Scale ◽  
Intelligent Vehicle ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Q Learning ◽  
Model Free ◽  
Control Actions

Reinforcement learning (RL) based techniques have been employed for the tracking and adaptive cruise control of a small-scale vehicle with the aim to transfer the obtained knowledge to a full-scale intelligent vehicle in the near future. Unlike most other control techniques, the purpose of this study is to seek a practical method that enables the vehicle, in the real environment and in real time, to learn the control behavior on its own while adapting to the changing circumstances. In this context, it is necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. Meanwhile, in order to realize adaptive cruise control specifically, a set of symmetrical control actions consisting of steering angle and vehicle speed needs to be optimized simultaneously. In this paper, firstly, the experimental setup of the small-scale intelligent vehicle is introduced. Subsequently, three model-free RL algorithm are conducted to develop and finally form the strategy to keep the vehicle within its lanes at constant and top velocity. Furthermore, a model-based RL strategy is compared that incorporates learning from real experience and planning from simulated experience. Finally, a Q-learning based adaptive cruise control strategy is intermixed to the existing tracking control architecture to allow the vehicle slow-down in the curve and accelerate on straightaways. The experimental results show that the Q-learning and Sarsa (λ) algorithms can achieve a better tracking behavior than the conventional Sarsa, and Q-learning outperform Sarsa (λ) in terms of computational complexity. The Dyna-Q method performs similarly with the Sarsa (λ) algorithms, but with a significant reduction of computational time. Compared with a fine-tuned proportion integration differentiation (PID) controller, the good-balanced Q-learning is seen to perform better and it can also be easily applied to control problems with over one control actions.

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