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.

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.

Do Autonomous Vehicle Driving Styles Affect User State?: A Preliminary Investigation

2019 ◽  
Author(s):  
Yuan Shi ◽  
Wenhui Huang ◽  
Federico Cheli ◽  
Monica Bordegoni ◽  
Giandomenico Caruso
Keyword(s):  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Autonomous Systems ◽  
Autonomous Driving ◽  
Point Of View ◽  
Automated Driving ◽  
Perspective View ◽  
Self Assessment ◽  
Driving Experience

Abstract A bursting number of achievements in the autonomous vehicle industry have been obtained during the past decades. Various systems have been developed to make automated driving possible. Due to the algorithm used in the autonomous vehicle system, the performance of the vehicle differs from one to another. However, very few studies have given insight into the influence caused by implementing different algorithms from a human factors point of view. Two systems based on two algorithms with different characteristics are utilized to generate the two driving styles of the autonomous vehicle, which are implemented into a driving simulator in order to create the autonomous driving experience. User’s skin conductance (SC) data, which enables the evaluation of user’s cognitive workload and mental stress were recorded and analyzed. Subjective measures were applied by filling out Swedish occupational fatigue inventory (SOFI-20) to get a user self-reporting perspective view of their behavior changes along with the experiments. The results showed that human’s states were affected by the driving styles of different autonomous systems, especially in the period of speed variation. By analyzing users’ self-assessment data, a correlation was observed between the user “Sleepiness” and the driving style of the autonomous vehicle. These results would be meaningful for the future development of the autonomous vehicle systems, in terms of balancing the performance of the vehicle and user’s experience.

scholarly journals Sequential lidar sensor system simulation: a modular approach for simulation-based safety validation of automated driving

2020 ◽  
Vol 5 (3-4) ◽  
pp. 187-197
Author(s):  
Philipp Rosenberger ◽  
Martin Friedrich Holder ◽  
Nicodemo Cianciaruso ◽  
Philip Aust ◽  
Jonas Franz Tamm-Morschel ◽  
...  
Keyword(s):  
Simulation Models ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Modular Approach ◽  
Active Perception ◽  
Automated Driving ◽  
Test Procedures ◽  
Sequential Approach ◽  
Simulation Based ◽  
Tracking Model

Abstract Validating safety is an unsolved challenge before autonomous driving on public roads is possible. Since only the use of simulation-based test procedures can lead to an economically viable solution for safety validation, computationally efficient simulation models with validated fidelity are demanded. A central part of the overall simulation tool chain is the simulation of the perception components. In this work, a sequential modular approach for simulation of active perception sensor systems is presented on the example of lidar. It enables the required level of fidelity of synthetic object list data for safety validation using beforehand simulated point clouds. The elaborated framework around the sequential modules provides standardized interfaces packaging for co-simulation such as Open Simulation Interface (OSI) and Functional Mockup Interface (FMI), while providing a new level of modularity, testability, interchangeability, and distributability. The fidelity of the sequential approach is demonstrated on an everyday scenario at an intersection that is performed in reality at first and reproduced in simulation afterwards. The synthetic point cloud is generated by a sensor model with high fidelity and processed by a tracking model afterwards, which, therefore, outputs bounding boxes and trajectories that are close to reality.

scholarly journals Phenomenological Modelling of Camera Performance for Road Marking Detection

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 194
Author(s):  
Hexuan Li ◽  
Kanuric Tarik ◽  
Sadegh Arefnezhad ◽  
Zoltan Ferenc Magosi ◽  
Christoph Wellershaus ◽  
...  
Keyword(s):  
Neural Network ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Computational Effort ◽  
Lane Detection ◽  
Physical Models ◽  
Automated Driving ◽  
Illumination Variation ◽  
Detection Model ◽  
The Difference

With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.

scholarly journals Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 42
Author(s):  
Mohammed Al-Nuaimi ◽  
Sapto Wibowo ◽  
Hongyang Qu ◽  
Jonathan Aitken ◽  
Sandor Veres
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Practical Implementation ◽  
Model Checker ◽  
Multi Agent Systems ◽  
Time Operation ◽  
Bdi Agent ◽  
Perception System ◽  
Probability Of Success

The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

scholarly journals Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 943 ◽  
Author(s):  
Il Bae ◽  
Jaeyoung Moon ◽  
Jeongseok Seo
Keyword(s):  
Real Time ◽  
High Performance ◽  
Autonomous Driving ◽  
Planning Method ◽  
Dynamics Simulation ◽  
Driving Experience ◽  
Optimal Velocity ◽  
Planning Approach ◽  
Velocity Planning ◽  
Time Optimal

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

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