Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

An Integrated Approach to Colorectal Anastomotic Leakage

__Abstract__ Colorectal anastomotic leakage (CAL) still remains a frequent and most dangerous complication after gastrointestinal surgery, occurring in 4-33% patients and contributing to one third of postoperative mortality. CAL substantially prolongs hospital stay ? by one to two weeks - and greatly increases medical costs by as much as $24,000 within the first period of hospitalization, thereby approximately tripling the expenditure relative to that of patients without CAL. Due to the high risk of postoperative mortality, substantial efforts have been made to investigate means of preventing and detecting CAL. In recent decades, however, even with substantial improvements in surgical technique, no clear decrease in CAL rate has been achieved. Much effort has been devoted to selecting patients with higher risks of CAL, and many risk factors have been identified, such as being male, smoking, alcohol abuse, obesity, a high American Society of Anesthesiologists (ASA) score, low level (e.g. rectal) anastomosis, tumor stage, urgent operation, increased blood loss, and prolonged duration of surgery have been revealed. Previous studies by our research group also reported several novel risk factors including after-hours surgery, and long-term and preoperative administration of corticosteroids. However, these risk factors seem to cover most patients and thus may have limited value in the preoperative selection of patients

Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles' behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM

Cytokines as Early Markers of Colorectal Anastomotic Leakage: A Systematic Review and Meta-Analysis

Purpose. Colorectal anastomotic leakage (CAL) is one of the most severe complications after colorectal surgery. This meta-analysis evaluates whether systemic or peritoneal inflammatory cytokines may contribute to early detection of CAL. Methods. Systematic literature search was performed in the acknowledged medical databases according to the PRISMA guidelines to identify studies evaluating systemic and peritoneal levels of TNF, IL-1β, IL-6, and IL-10 for early detection of CAL. Means and standard deviations of systemic and peritoneal cytokine levels were extracted, respectively, for patients with and without CAL. The meta-analysis of the mean differences was carried out for each postoperative day using Review Manager. Results. Seven articles were included. The meta-analysis was performed with 5 articles evaluating peritoneal cytokine levels. Peritoneal levels of IL-6 were significantly higher in patients with CAL compared to patients without CAL on postoperative days 1, 2, and 3 (P<0.05). Similar results were found for peritoneal levels of TNF but on postoperative days 3, 4, and 5 (P<0.05). The articles regarding systemic cytokine levels did not report any significant difference accordingly. Conclusion. Increased postoperative levels of peritoneal IL-6 and TNF are significantly associated with CAL and may contribute to its early detection