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.

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 Adaptive Semi-Active Suspension and Cruise Control through LPV Technique

2020 ◽  
Vol 11 (1) ◽  
pp. 290
Author(s):  
Hakan Basargan ◽  
András Mihály ◽  
Péter Gáspár ◽  
Olivier Sename
Keyword(s):  
Control Systems ◽  
Active Suspension ◽  
Cruise Control ◽  
Linear Parameter Varying ◽  
Trade Off ◽  
Look Ahead ◽  
Vehicle Velocity ◽  
Suspension Control ◽  
Road Data ◽  
Terrain Characteristics

Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system.

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

2020 ◽  
Author(s):  
Jun Meng ◽  
Jingfang Fan ◽  
Josef Ludescher ◽  
Ankit Agarwala ◽  
Xiaosong Chen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Statistical Prediction ◽  
Analysis Tool ◽  
Strong Positive Correlation ◽  
Near Surface ◽  
Spring Predictability Barrier ◽  
Prediction Horizon ◽  
The Individual

<p>The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows to forecast the magnitude of an El Niño with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error = 0.23°C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasts a weak El Niño with a magnitude of 1.11±0.23°C.  Our framework presented here not only facilitates a long–term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.</p>

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

An Adaptive Cruise Control Using Wheel Torque Management Technique

1998 ◽  
Author(s):  
Satoru Kuragaki ◽  
Hiroshi Kuroda ◽  
Toshimichi Minowa ◽  
Mitsuo Kayano ◽  
Tokuji Yoshikawa ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Management Technique ◽  
Cruise Control ◽  
Wheel Torque

scholarly journals Assessing the Predictive Value of Traffic Count Data in the Imputation of On-Street Parking Occupancy in Amsterdam

2021 ◽  
pp. 036119812110296
Author(s):  
Pablo Martín Calvo ◽  
Bas Schotten ◽  
Elenna R. Dugundji
Keyword(s):  
Prediction Models ◽  
Explanatory Variable ◽  
Traffic Data ◽  
Forecasting Models ◽  
Probe Vehicle ◽  
The Social ◽  
Huge Impact ◽  
Potential Improvement ◽  
Occupancy Prediction ◽  
The City

On-street parking policies have a huge impact on the social welfare of citizens. Accurate parking occupancy data across time and space is required to properly set such policies. Different imputation and forecasting models are required to obtain this data in cities that use probe vehicle measurements, such as Amsterdam. In this paper, the usage of traffic data as an explanatory variable is assessed as a potential improvement to existing parking occupancy prediction models. Traffic counts were obtained from 164 traffic cameras throughout the city. Existing models for predicting parking occupancy were reproduced in experiments with and without traffic data, and their performance was compared. Results indicated that (i) traffic data are indeed a useful predictor and improves performance of existing models; (ii) performance does not improve linearly with an increase in the number of counting points; and (iii) placement of the cameras does not have a significant impact on performance.

scholarly journals A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

2021 ◽  
Vol 33 (4) ◽  
pp. 593-608
Author(s):  
Chuhao Zhou ◽  
Peiqun Lin ◽  
Xukun Lin ◽  
Yang Cheng
Keyword(s):  
Neural Network ◽  
Road Network ◽  
Large Scale ◽  
Prediction Models ◽  
Short Term Memory ◽  
Traffic Data ◽  
Traffic Operations ◽  
Forecast Horizon ◽  
Temporal Characteristics ◽  
Long Short Term Memory

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN.

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