Automated Vehicle Highway Merging: Motion Planning via Adaptive Interactive Mixed-Integer MPC

A new motion planning framework for automated highway merging is presented in this paper. To plan the merge and predict the motion of the neighboring vehicle, the ego automated vehicle solves a joint optimization of both vehicle costs over a receding horizon. The non-convex nature of feasible regions and lane discipline is handled by introducing integer decision variables resulting in a mixed integer quadratic programming (MIQP) formulation of the model predictive control (MPC) problem. Furthermore, the ego uses an inverse optimal control approach to impute the weights of neighboring vehicle cost by observing the neighbor's recent motion and adapts its solution accordingly. We call this adaptive interactive mixed integer MPC (aiMPC). Simulation results show the effectiveness of the proposed framework.Comment: Submitted to American Control Conferenc

Interactive Motion Planning for Autonomous Vehicles via Adaptive Interactive MPC

This article presents a new optimal control-based interactive motion planning algorithm for an autonomous vehicle interacting with a human-driven vehicle. The ego vehicle solves a joint optimization problem for its motion planning involving costs and coupled constraints of both vehicles and applies its own actions. The non-convex feasible region and lane discipline are handled by introducing integer decision variables and the resulting optimization problem is a mixed-integer quadratic program (MIQP) which is implemented via model predictive control (MPC). Furthermore, the ego vehicle imputes the cost of human-driven neighboring vehicle (NV) using an inverse optimal control method based on Karush-Kuhn-Tucker (KKT) conditions and adapts the joint optimization cost accordingly. We call the algorithm adaptive interactive mixed-integer MPC (aiMPC). Its interaction with human subjects driving the NV in a mandatory lane change scenario is tested in a developed software-and-human-in-the-loop simulator. Results show the effectiveness of the presented algorithm in terms of enhanced mobility of both the vehicles compared to baseline methods

System and method for utilizing traffic signal information for improving fuel economy and reducing trip time

A system and method for utilizing traffic signal information to reduce wait time at traffic signals and to reduce fuel use is disclosed. Traffic signal timing data can be received from traffic signals or from a central server to determine a reference velocity for a vehicle to travel through a plurality of traffic signals. The reference velocity can then be provided to the driver so that the driver can manually control the vehicle at a velocity close to the reference velocity. The techniques of the present disclosure also can be used in connection with a cruise control system to control the velocity of a vehicle to be close to the reference velocity

Considerate and Cooperative Model Predictive Control for Energy-Efficient Truck Platooning of Heterogeneous Fleets

Connectivity-enabled automation of distributed control systems allow for better anticipation of system disturbances and better prediction of the effects of actuator limitations on individual agents when incorporating a model. Automated convoy of heavy-duty trucks in the form of platooning is one such application designed to maintain close gaps between trucks to exploit drafting benefits and improve fuel economy, and has traditionally been handled with classically-designed connected and adaptive cruise control (CACC). This paper is motivated by demonstrated limitations of such a control strategy, in which a classical CACC was unable to efficiently handle real-world road grade and velocity transient disturbances without the assistance of fleet operator intervention, and is non-adaptive to varied hardware and loading conditions of the operating truck. This automation strategy is addressed by forming a cooperative model predictive control (MPC) for eco-platooning that considers interactions with trailing trucks to incentivize platoon harmonization under road disturbances, velocity transients, and engine limitations, and further improves energy economy by reducing unnecessary engine effort. This is accomplished for each truck by sharing load, maximum engine power, transmission ratios, control states, and intended trajectories with its nearest neighbors. The performance of the considerate and cooperative strategy was demonstrated on a real-world driving scenario against a similar non-considerate control strategy, and overall it was found that the considerate strategy significantly improved harmonization between the platooned trucks in a real-time implementable manner.Comment: Appears in IEEE ACC 2022. 6 pages, 6 figure

Systems and Methods for Predicting Traffic Signal Information

Systems and methods for predicting traffic signal information are provided. An exemplary method includes obtaining data from a plurality of types of sources and analyzing the data to predict states of a plurality of traffic signals. The data include crowdsourced data. The predictive traffic signal information may be used to adjust an operation of an on-board system of a vehicle

Experimental Modeling of Cyclists Fatigue and Recovery Dynamics Enabling Optimal Pacing in a Time Trial

Improving a cyclist performance during a time-trial effort has been a challenge for sport scientists for several decades. There has been a lot of work on understanding the physiological concepts behind it. The concepts of Critical Power (CP) and Anaerobic Work Capacity (AWC) have been discussed often in recent cycling performance related articles. CP is a power that can be maintained by a cyclist for a long time; meaning pedaling at or below this limit, theoretically, can be continued for infinite amount of time. However, there is a limited source of energy for generating power above CP. This limited energy source is AWC. After burning energy from this tank, a cyclist can recover some by pedaling below CP. In this paper we utilize the concepts of CP and AWC to mathematically model muscle fatigue and recovery of a cyclist. Then, the models are used to formulate an optimal control problem for a time trial effort on a 10.3 km course located in Greenville SC. The course is simulated in a laboratory environment using a CompuTrainer. At the end, the optimal simulation results are compared to the performance of one subject on CompuTrainer.Comment: 6 pages, 8 figure

Two Different Endotracheal Tube Securing Techniques: Fixing Bandage vs. Adhesive Tape

Introduction: Emergency physicians should secure Endotracheal tubes (ETT) properly in order to prevent unplanned extubation (UE) and its complications. Despite various available endotracheal tube holders, using bandages or tape are still the most common methods used in this regards. Objective: This study aimed to compare adhesive tape (AT) versus fixing bandage (FB) method in terms of properly securing ETT. Methods: This was an observational longitudinal trial. All patients older than 15-years-old admitted to the ED who had indication for ETT insertion were eligible. Patients were randomly assigned to one of the two groups in which AT or FB was applied. All patients were observed thoroughly in the first 24 hours after intubation. Using a pre-prepared checklist, encountered UE rate and other data were recorded. Results: Seventy-two patients with the mean age of 55.98 ± 18.39 years were finally evaluated of which 38 cases (52.8%) were male. In total, 12% of patients in our study experienced unplanned extubation. Less than 12% of the patients experienced complete UE; there was no statistically significant difference between the two groups (p = 0.24). Comparison of UE with age showed no significant difference (p = 0.89). Male patients experienced more UE, but this was not statistically significant (p = 0.44). Conclusion: It is likely that whether the AT method or FB was applied for securing the ETT in emergency departments, there was no significant difference in rates of unplanned extubation

Prediction on Travel-Time Distribution for Freeways Using Online Expectation Maximization Algorithm

5 3300 words + 8 figure(s) + 0 table(s) ⇒ 5300 'words' 1 2 ABSTRACT This paper presents a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribution for travel time. On-ramp and mainline flow profiles are collected from loop detectors, along with their uncertainties. The probability distribution is generated using Monte 5 Carlo simulation and the Online Expectation Maximization clustering algorithm. The simulation is implemented with a reasonable stopping criterion in order to reduce sample size requirement. Results show that the approach is able to generate an accurate multimodal distribution for traveltime. Future improvements are also discussed