Multi Time-Scale Engine and Powertrain Control for Autonomous Vehicles via Lagrange Multipliers

2021 ◽  
Author(s):  
Stephen Boyle ◽  
Stephanie Stockar
Keyword(s):  
Fuel Economy ◽  
Lagrange Multipliers ◽  
Autonomous Vehicles ◽  
Trajectory Optimization ◽  
Actuator Saturation ◽  
Control Strategies ◽  
Computation Time ◽  
Traffic Light ◽  
Engine Torque ◽  
Powertrain Control

Abstract Connected and Autonomous vehicles (CAVs) have the ability to use information obtained via Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle communication (V2V), and sensors to improve their fuel economy through predictive strategies, including velocity trajectory optimization and optimal traffic light arrival and departure. These powertrain control strategies operate on a slow timescale relative to the engine dynamics, hence assume that the engine torque production is instantaneous. This assumption results in a torque command profile that may lead to engine dynamics constraint violation, actuator saturation, poor tracking performance, decreased efficiency, poor drivability, and increased emissions. To address this issue, a supplemental controller based on an iterative hierarchical Model Predictive Control (MPC) is proposed in this paper. The constraint satisfaction is achieved through a novel two-way communication of the Lagrange multipliers. The proposed methodology is demonstrated on an autonomous Diesel semi-truck on two maneuvers. Compared to a traditional centralized approach, the proposed method achieves systematic constraints satisfaction with negligible effect on fuel economy, less than 1%, and significantly improved computation time, more than 10 times.

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

scholarly journals Singularly Perturbation Method Applied To Multivariable PID Controller Design

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Mashitah Che Razali ◽  
Norhaliza Abdul Wahab ◽  
P. Balaguer ◽  
M. F. Rahmat ◽  
Sharatul Izah Samsudin
Keyword(s):  
Perturbation Method ◽  
Pid Controller ◽  
Closed Loop ◽  
Controller Design ◽  
Control Strategies ◽  
High Reliability ◽  
Treatment Plant ◽  
Computation Time ◽  
Singularly Perturbed ◽  
Singularly Perturbation

Proportional integral derivative (PID) controllers are commonly used in process industries due to their simple structure and high reliability. Efficient tuning is one of the relevant issues of PID controller type. The tuning process always becomes a challenging matter especially for multivariable system and to obtain the best control tuning for different time scales system. This motivates the use of singularly perturbation method into the multivariable PID (MPID) controller designs. In this work, wastewater treatment plant and Newell and Lee evaporator were considered as system case studies. Four MPID control strategies, Davison, Penttinen-Koivo, Maciejowski, and Combined methods, were applied into the systems. The singularly perturbation method based on Naidu and Jian Niu algorithms was applied into MPID control design. It was found that the singularly perturbed system obtained by Naidu method was able to maintain the system characteristic and hence was applied into the design of MPID controllers. The closed loop performance and process interactions were analyzed. It is observed that less computation time is required for singularly perturbed MPID controller compared to the conventional MPID controller. The closed loop performance shows good transient responses, low steady state error, and less process interaction when using singularly perturbed MPID controller.

scholarly journals TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY

2016 ◽  
Vol III-1 ◽  
pp. 191-200
Author(s):  
S. Hosseinyalmdary ◽  
A. Yilmaz
Keyword(s):  
Prior Knowledge ◽  
Pose Estimation ◽  
Open Problem ◽  
Autonomous Vehicles ◽  
Conic Section ◽  
Traffic Light ◽  
Light Detection ◽  
State Recognition ◽  
Traffic Lights ◽  
Stereo Cameras

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.

Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains

2020 ◽  
Author(s):  
James Dallas ◽  
Yifan Weng ◽  
Tulga Ersal
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Soft Soil ◽  
Computation Time ◽  
Operating Conditions ◽  
The Neural Network ◽  
Vehicle Response ◽  
The Impact

Abstract In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.

scholarly journals Model-based assessment of the impact of driver-assist vehicles using kinetic theory

2020 ◽  
Vol 71 (5) ◽  
Author(s):  
Benedetto Piccoli ◽  
Andrea Tosin ◽  
Mattia Zanella
Keyword(s):  
Kinetic Theory ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Field Studies ◽  
Kinetic Approach ◽  
Kinetic Description ◽  
Traffic Models ◽  
Nice Agreement ◽  
Theoretical Evidence ◽  
The Impact

Abstract In this paper, we consider a kinetic description of follow-the-leader traffic models, which we use to study the effect of vehicle-wise driver-assist control strategies at various scales, from that of the local traffic up to that of the macroscopic stream of vehicles. We provide theoretical evidence of the fact that some typical control strategies, such as the alignment of the speeds and the optimisation of the time headways, impact on the local traffic features (for instance, the speed and headway dispersion responsible for local traffic instabilities) but have virtually no effect on the observable macroscopic traffic trends (for instance, the flux/throughput of vehicles). This unobvious conclusion, which is in very nice agreement with recent field studies on autonomous vehicles, suggests that the kinetic approach may be a valid tool for an organic multiscale investigation and possibly the design of driver-assist algorithms.

scholarly journals Chance-Constrained Optimal Path Planning With Obstacles

2011 ◽  
Vol 27 (6) ◽  
pp. 1080-1094 ◽  
Author(s):  
Lars Blackmore ◽  
Masahiro Ono ◽  
Brian C. Williams
Keyword(s):  
Autonomous Vehicles ◽  
Solution Method ◽  
Optimal Path ◽  
Computation Time ◽  
Global Optimality ◽  
New Approach ◽  
Optimal Path Planning ◽  
Modeling Errors ◽  
Chance Constrained Approach ◽  
Probability Of Collision

Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.

A Numerical Study of an Electrically Assisted Boosting System for Turbocharged Diesel Engines

2019 ◽  
Author(s):  
Yifan Men ◽  
Jason B. Martz ◽  
Eric Curtis ◽  
Guoming G. Zhu
Keyword(s):  
Fuel Economy ◽  
Diesel Engines ◽  
Numerical Study ◽  
Peak Torque ◽  
Engine Fuel ◽  
Engine Torque ◽  
Low Speed ◽  
Electrically Assisted ◽  
Torque Response ◽  
Variable Geometry Turbine

Abstract Modern diesel engines are normally turbocharged in order to achieve desired fuel economy and meet emission requirements. The well-known “turbo-lag”, delayed engine torque response to driver’s demand, is the main disadvantage for turbocharged engines operated under transient conditions. In addition, at low engine speed, the peak engine output torque is heavily limited by the available turbine energy. As a result, turbocharged engines have degraded peak torque at low speed and slow transient responses in general. Various technologies (variable geometry turbine, electrically assisted turbocharger, hydraulically assisted turbocharger, etc.) have been developed to improve transient response and low-speed torque performance. This paper presents a numerical study of an electrically assisted boosting (eBoost) system for a turbocharged diesel engine through 1-D simulations. This study focuses on two main areas: the electrical compensation at steady-state and turbo-lag reduction under transient operation. It is shown that the eBoost system is capable of increasing engine fuel economy at mid-speed and greatly improving low-speed peak torque. In addition, the eBoost system improves engine transient performance by reducing response time up to 60%.

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