Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

An Integratable V-X Communication based Conventional Vehicle Fuel Optimization model for distance based Ecological Driving Scheme

This thesis paper proposes An Integratable V-X Communication-based Conventional Vehicle Fuel Optimization model for real-time traffic conditions. Before departure, the speed profile for an entire route is optimized using signal phase and timing (SP AT) information and location of traffic lights to provide smooth transitions at traffic signal intersections. In this study, we are going to develop ”nonstop” optimal speed model that can be integrated to existing distance based eco-driving schemes. The initial simulation is done using MATLAB to evaluate optimal speed, fuel economy, the travel time of the ”nonstop” model and the results are compared with the optimization results from distance based eco-driving scheme which uses an estimation of distribution algorithm (EDA). Further integration compatibility of ”nonstop” model with the distance based eco-driving scheme is analyzed.Master of Science in EngineeringComputer Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146734/1/Thesis 11-13-2018.pdfDescription of Thesis 11-13-2018.pdf : Thesi

Velocity Optimization in Connected Autonomous Vehicles and its Impact on Surrounding Traffic

Connected Autonomous Vehicles are equipped with the capabilities of autonomous navigation, Vehicle to Vehicle, and Vehicle to Infrastructure communication, which have the potential to improve fuel and/ or energy efficiency. Velocity optimization is a driving technique that aims to follow a velocity profile that minimizes fuel consumption, energy consumption, idling at traffic lights, and overall trip time. Velocity optimization can be implemented in CAVs by utilizing V2I and V2V capabilities, and optimal control techniques.As CAVs become more ubiquitous, they are likely to interact closely with human driven cars. In such a scenario, it is important to find the right trade-off between safety and efficiency, as safety constraints may restrict efficient actions and vice-versa. Vehicle control systems that are heavily biased towards efficiency, may result in conservativeness and rear-ending effects in CAVs, rendering their behavior unpredictable for human drivers, which may result in collisions, compromise safety and obstruct the surrounding traffic. Through this research, we have proposed a velocity optimization strategy that optimizes the velocity profile for fuel consumption, without significantly compromising safety and affecting the traffic flow. A Model Predictive Controller is designed to compute the optimal velocity profile based on fuel consumption and impact to the surrounding traffic. A mathematical control parameter is introduced for deterministic control of impact on traffic flow. An iterative convex optimization approach is adopted for online solution of the optimal control problem. A simulation case study is presented to demonstrate fuel saving capability and reduced impact on the surrounding traffic flow, of the proposed control system

Reducing the energy consumption of electric buses with design choices and predictive driving

Peer reviewe

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

An Enhanced Predictive Cruise Control System Design with Data-Driven Traffic Prediction

The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-board, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated