Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles
Abstract
:1. Introduction
2. Previous Works
2.1. Synergy Opportunities of New Technologies
2.2. Road Perception
3. State of the Art
4. Methodology
4.1. Work Process Overview
4.2. Experimentation and Test Route
4.3. Test Vehicle
5. Features and Data Preparation
5.1. Data Pre-Processing
5.2. Variable’s Information and Features Analysis
5.2.1. Driver Effect in the Driving Environment Classification
5.2.2. Electric Power Variables
5.3. Data Processing and Environment Parameters
6. Data Analysis
6.1. Analysis of Samples from Pure Datasets
6.2. Data Segmentation into Subsamples
7. Results and Discussion
7.1. Feature Importance Results based on Mean Decrease in Impurity
7.2. Classifiers Benchmark
7.3. Processing and Analysis of Samples in 2-s Windows
8. Conclusions
9. Future Work
- The integration of ML classifiers into intelligent driving systems for real-time awareness in pre-defined scenarios and the use of this information for calculating optimal energy-use strategies (Figure 15).
- The automatic classification of new scenarios, using ML strategies such as unsupervised learning for the clustering of classes with common characteristics in terms of drivability and energetic-related driving-style requirements.
- The same methodology exposed in this work can be tested again and improved by including more driving scenarios and routes with more complex characteristics and interactions with other players of the driving environment.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Acronym | Definition |
---|---|
ML | Machine Learning |
KNN | k-nearest neighbors |
RF | Random Forest |
SVC | Support Vector Classifier |
SVM | Support Vector Machine |
RBF | Radial Base Function |
IMU | Inertial Measurement Unit |
Feature Name | Description |
---|---|
Vmn [m/s] | Mean Longitudinal Velocity [44] |
Vsd [m/s] | Standard deviation of longitudinal velocity [44] |
Amn(+) [m/s2] | Mean of the positive acceleration in X. It is related to the use of the throttle [44] |
Asd(+) | Standard deviation of positive acceleration at X [44] |
Axsd [m/s2] | Standard deviation of longitudinal acceleration [44] |
Axmn [m/s2] | Mean longitudinal acceleration [44] |
Aymn [m/s2] | Mean of vertical acceleration |
Aysd [m/s2] | Standard deviation of vertical acceleration |
Azmn [m/s2] | Average lateral acceleration |
Azsd [m/s2] | Standard deviation of lateral acceleration |
Abrmn [m/s2] | Mean of braking acceleration (negative values of longitudinal acceleration) [44] |
Abrsd | Standard deviation of braking acceleration [44] |
RPA | Relative positive acceleration [38] |
Potmn | Average power during the test, measured from batteries |
Potsd | Standard deviation of electrical power |
Jerk (X, Y, Z) [m/s3] | Statistical value for comfort, relates the standard deviation and the average of the signal derived from the acceleration in each of the coordinate axes [40] |
Rollmn | Roll average |
Rollsd | Roll standard deviation |
Pitchmn | Pitch average |
Pitchsd | Pitch standard deviation |
Energy [Wh/km] | Electric energy measured at the motor, given for each kilometer travelled |
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Article | Authors | Functions | Model | Methods | Evaluation |
---|---|---|---|---|---|
Increasing the Fuel Economy of Connected and Autonomous Lithium-Ion Electrified Vehicles | Asher et al., 2018 [2] | Energy Management, V2V, environment Perception | Vehicle Dynamics Model, Energy Management Model | Dynamic programming and Pontryagin’s Minimization Principle | SIMULATION using models to compare results of different control strategies |
On the Optimal Speed Profile for Eco-Driving on Curved Roads | Ding et al., 2019 [8] | Velocity profile optimization for curved roads | Vehicle Dynamics Model, Fuel Consumption Model | Dynamic programming Optimization | Algorithm verification using co-simulation of CarSim and Matlab/Simulink |
Design and Implementation of Ecological Adaptive Cruise Control for Autonomous Driving with Communication to Traffic Lights | Bae et al., 2018 [15] | V2I, EAD *, Surrounding traffic consideration | Vehicle Dynamics Model | Robust Model Predictive Control | ACC ** tested in a Hardware in the Loop setup with SPaT *** information |
Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions | Min et al., 2020 [26] | Decelerations predictions | Deep learning | Deep neural network (RNN, LSTM, conventional neural network), K means clustering method | Vehicle velocity, relative distance between the vehicle and the traffic light, reference acceleration |
Quantifying the Impact of Traffic on Electric Vehicle Efficiency | Jonas et al., 2022 [27] | Impact of traffic on Electric Vehicle efficiency | Statistical models | Regression models, ANOVA | Total energy consumption, total distance, Average consumption per mile, Mean variation in speed, Mean variation in acceleration, Mean variation in jerk |
Road surface real-time detection based on Raspberry Pi and recurrent neural networks | Wang et al., 2021 [28] | Road surface detection | Recurrent Neural Network | Allan variance, Machine learning algorithms (KNN, L2 logistic regression, Decision tree, SVM cross validation), Deep learning Algorithms (LSTM, RNN) | Three axis accelerometer (x, y, z) and three axis gyroscope (x, y, z) |
An IMU-based traffic and road condition monitoring system | Lei et al., 2018 [29] | Traffic and road condition monitoring system | Fast Fourier Transform | Least squares optimization for speed estimation considering sensor bias, DCM filter for attitude angle estimation | Relation between vertical acceleration and Present Serviceability Rating (PSR) |
Map Matching and Lane Detection Based on Markovian Behavior, GIS, and IMU Data | Trogh et al., 2020 [30] | Map matching and lane detection | Markovian behavior | Viterbi (hidden Markov model) | Direction of road segments, maximum allowed speed per road segment, and driving behavior |
Safe and Ecological Speed Profile Planning Algorithm for Autonomous Vehicles Using a Parametric Multi-objective Optimization Procedure | Orfila et al., 2019 [31] | Velocity profile optimization for predefined route | Data-driven approach | Global Optimization using Simulated Annealing | Velocity profile data comparison—Algorithm results Vs human drivers experimental data |
Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning | Hu et al., 2018 [32] | Energy Management | HEV Model | Deep Reinforcement Learning | Trained control Algorithm tested in MATHLAB and ADVISOR Co-simulation |
Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles | Jeoung et al., 2019 [33] | Rule based controller, Energy Management | Parallel Type 2 HEV Model | Dynamic programming and Pontryagin’s Minimization Principle | Controller algorithm evaluation in SIMULATION HEV MODEL |
A Learning-Based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles | Kazemi et al., 2018 [34] | CACC, Surrounding traffic consideration, V2V | Data-driven approach | Artificial Neural Network | Model Predictive Controller evaluated in SIMULATED test runs from a real driving tests dataset |
Cooperative Adaptive Cruise Control with Fuel Efficiency Using PMP Technique | Rasool et al., 2019 [35] | CACC, Surrounding traffic consideration | Platoon model, ICE power train Model | Pontryagin’s Minimization Principle | Validation of the controller with the models in a SIMULATION |
On combining Big Data and machine learning to support eco-driving behaviors | Delnevo et al., 2019 [36] | HMI, ADAS | Data-driven approach | Machine Learning | Algorithm testing in SIMULATION using real data |
Real-Time Optimal Eco-Driving for Hybrid-Electric Vehicles | Zhu et al., 2019 [37] | ADAS, HEV | Data-driven approach | Dynamic programming Optimization/Artificial Neural Network | Mutual Validation of the obtained speed profiles using DPO and ANN methods |
Environment classification using machine learning methods for eco-driving strategies in intelligent vehicles. | (THIS PROPOSED WORK) | Driving Environment classification | Machine learning models | Machine Learning algorithms (KNN, SVM, Decision tree) | Linear velocity, three axis acceleration, energy consumption, jerk, roll, pitch |
Motor | |
Power | 15 HP |
Nominal Voltage | 76 V |
Nominal current | 115 A |
Peak current | 132 A |
Max speed | 3000 RPM |
Battery | |
Total voltage | 96 V |
Number of cells | 32 |
Total Energy Capacity | 28,800 Wh |
Discharge rating 2C | 0.5 |
Discharge current | 150 A |
Vehicle brute mass | 1370 kg |
Wheels’ dynamic radius | 0.25019 m |
Rolling resistance coefficient | 0.014984 |
Aerodynamic drag coefficient | 0.436 |
Vehicle frontal Area | 2.15 m2 |
Gearbox efficiency | 0.95 |
Axle differential efficiency | 0.95 |
Top Speed | 100 km/h |
KNN F1-Score | SVM F1-Score | Support Samples | |
---|---|---|---|
Flatland | 0.8 | 0.99 | 45 |
Cobblestone | 0.7 | 1.0 | 36 |
Transit | 0.88 | 0.99 | 38 |
Accuracy | 0.8 | 0.99 | 119 |
Macro avg. | 0.8 | 0.99 | 119 |
KNN k = 19 | SVM (RBF) | Random Forest | |
---|---|---|---|
Test accuracy | 93.2% | 88.9% | 90.7% |
Precision | 93% | 89% | 91% |
Recall | 94% | 89% | 91% |
F1-score | 93% | 89% | 91% |
Support samples | 8535 | 8535 | 8535 |
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Julio-Rodríguez, J.d.C.; Rojas-Ruiz, C.A.; Santana-Díaz, A.; Bustamante-Bello, M.R.; Ramirez-Mendoza, R.A. Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles. Appl. Sci. 2022, 12, 5578. https://doi.org/10.3390/app12115578
Julio-Rodríguez JdC, Rojas-Ruiz CA, Santana-Díaz A, Bustamante-Bello MR, Ramirez-Mendoza RA. Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles. Applied Sciences. 2022; 12(11):5578. https://doi.org/10.3390/app12115578
Chicago/Turabian StyleJulio-Rodríguez, Jose del C., Carlos A. Rojas-Ruiz, Alfredo Santana-Díaz, M. Rogelio Bustamante-Bello, and Ricardo A. Ramirez-Mendoza. 2022. "Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles" Applied Sciences 12, no. 11: 5578. https://doi.org/10.3390/app12115578
APA StyleJulio-Rodríguez, J. d. C., Rojas-Ruiz, C. A., Santana-Díaz, A., Bustamante-Bello, M. R., & Ramirez-Mendoza, R. A. (2022). Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles. Applied Sciences, 12(11), 5578. https://doi.org/10.3390/app12115578