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

Automatic Methodology for Multi-modal Trip Generation with Roadside LiDAR

Transportation planning based on historical data and methods has major limitations. Trip data canbe useful to increase the transportation safety of the specific sites and the process and programming purposes. One of the challenges in this regard is data collecting to gain an accurate analysis of land use development. The previous methods of data gathering such as human observational data counting and automatic methods like pneumatic tubes and video camera suffers some limitations that affect the accuracy of trip analysis which cause over mitigating or set some wrong rules and regulations. Light Detection and Ranging (LiDAR) sensing is a powerful tool that has been vastly used for mapping, safety, and medical applications. [1] Also, its application in transportation has drawn attention in recent years. However, LiDAR sense is yet to be further explored in trip generation. This study is an initial attempt to: 1) perform a LiDAR-based trip generation data gathering for a local area in midtown, Reno, and 2) analyze the resulting data based on the GIS software to develop a systematic plan for the case study and beyond

iDriving: Toward Safe and Efficient Infrastructure-directed Autonomous Driving

Autonomous driving will become pervasive in the coming decades. iDriving improves the safety of autonomous driving at intersections and increases efficiency by improving traffic throughput at intersections. In iDriving, roadside infrastructure remotely drives an autonomous vehicle at an intersection by offloading perception and planning from the vehicle to roadside infrastructure. To achieve this, iDriving must be able to process voluminous sensor data at full frame rate with a tail latency of less than 100 ms, without sacrificing accuracy. We describe algorithms and optimizations that enable it to achieve this goal using an accurate and lightweight perception component that reasons on composite views derived from overlapping sensors, and a planner that jointly plans trajectories for multiple vehicles. In our evaluations, iDriving always ensures safe passage of vehicles, while autonomous driving can only do so 27% of the time. iDriving also results in 5x lower wait times than other approaches because it enables traffic-light free intersections

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega trends in automotive engineering and they are strongly connected to each other [...

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