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

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

A self-learning intersection control system for connected and automated vehicles

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the chain impact of taking random actions in the training course, the trained model can deal with unprecedented volume circumstances, the main challenge in intersection management. Application of the model to a single-lane intersection with no turning movement as a proof-of-concept test reveals noticeable improvements in traffic measures compared to three other intersection control systems. A Spring Mass Damper (SMD) model is developed to control platooning behavior of CAVs. In the SMD model, each vehicle is assumed as a mass, coupled with its preceding vehicle with a spring and a damper. The spring constant and damper coefficient control the interaction between vehicles. Limitations on communication range and the number of vehicles in each platoon are applied in this model, and the SMD model controls intra-platoon and inter-platoon interactions. The simulation result for a regular highway section reveals that the proposed platooning algorithm increases the maximum throughput by 29% and 63% under 50% and 100% market penetration rate of CAVs. A merging section with different volume combinations on the main section and merging section and different market penetration rates of CAVs is also modeled to test inter-platoon spacing performance in accommodating merging vehicles. Noticeable travel time reduction is observed in both mainline and merging lanes and under all volume combinations in 80% and higher MPR of CAVs. For a more reliable assessment of the DSCLS, the model is applied to a more realistic intersection, including three approaching lanes in each direction and turning movements. The proposed algorithm decreases delay by 58%, 19%, and 13% in moderate, high, and extreme volume regimes, improving travel time accordingly. Comparison of safety measures reveals 28% improvement in Post Encroachment Time (PET) in the extreme volume regime and minor improvements in high and moderate volume regimes. Due to the limited acceleration and deceleration rates, the proposed model does not show a better performance in environmental measures, including fuel consumption and CO2 emission, compared to the conventional control systems. However, the DSCLS noticeably outperforms the other pixel-reservation counterpart control system, with limited acceleration and deceleration rates. The application of the model to a corridor of four interactions shows the same trends in traffic, safety, and environmental measures as the single intersection experiment. An automated intersection control system for platooning CAVs is developed by combining the two proposed models, which remarkably improves traffic and safety measures, specifically in extreme volume regimes compared to the regular DSCLS model

A Survey on platoon-based vehicular cyber-physical systems

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another one and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared to driving individually, such a platoon-based driving pattern can significantly improve the road capacity and energy efficiency. Moreover, with the emerging vehicular adhoc network (VANET), the performance of platoon in terms of road capacity, safety and energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of VANET. Such a complex system can be considered as a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on platoon-based VCPS. We first review the related work of platoon-based VCPS. We then introduce two elementary techniques involved in platoon-based VCPS: the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control (CACC), platoon-based vehicular communications, etc., and all of which are characterized by the tight coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions

Designing and Scheduling Cost-Efficient Tours by Using the Concept of Truck Platooning

Truck Platooning is a promising new technology to reduce the fuel consumption by around 15% via the exploitation of a preceding and digitally connected truck’s slipstream. However, the cost-efficient coordination of such platoons under consideration of mandatory EU driving time restrictions turns out to be a highly complex task. For this purpose, we provide a comprehensive literature review and formulate the exact EU-Truck Platooning Problem (EU-TPP) as an Integer Linear Program (ILP) which also features a hypothetical task-relieving effect for following drivers in a convoy. In order to increase the computational efficiency, we introduce an auxiliary constraint and two hierarchical planning-based matheuristic approaches: the Shortest Path Heuristic (SPH) and the Platoon Routing Heuristic (PRH). Besides a qualitative sensitivity analysis, we perform an extensive numerical study to investigate the impact of different critical influence factors on platooning, being of major political and economic interest. Our experiments with the EU-TPP suggest remarkable fuel cost savings of up to 10.83% without a 50% task relief, while its inclusion leads to additional personnel cost savings of up to even 31.86% at best with maximally 12 trucks to be coordinated in a recreated part of the European highway network. Moreover, we prove our matheuristics’ highly favorable character in terms of solution quality and processing time. Keywords: autonomous transport; Truck Platooning; driving time and rest periods; cost-efficient routing & scheduling; computational efficiency

A Rule Based Control Algorithm for on-Ramp Merge With Connected and Automated Vehicles

One of the designs for future highways with the flow of Connected Automated Vehicles (CAVs) cars will be a dedicated lane for the CAVs to form platoons and travel with higher speeds and lower headways. The connectivity will enable the formation of platoons of CAVs traveling beside non-platoon lanes. The advent of connectivity between vehicles and the infrastructure will enable advanced control strategies ̶ improving the performance of the traffic ̶ to be incorporated in the traffic system. The merge area in a multilane highway with CAVs is one of the sections which can be enhanced by the operation of a control system. In this research, a model is developed for investigating the effects of a Rule Based control strategy yielding a more efficient and systematic method for the vehicles joining the highway mainlines comprised of platoon and non-platoon lanes. The actions tested for assisting the merge process included deceleration in the mainlines and lane change to join a platoon in the platoon lane. The model directs every CAV entering a multi-lane highway from an on-ramp, to the rightmost lane of the highway based on the appropriate action which is selected according to the traffic demand conditions and location of the on-ramp vehicle. To account for car following behavior, the vehicles in the platoon lanes are assumed to have a simplified CACC (cooperative adaptive cruise control) and those in the non-platoon lanes the IDM+ car-following model. The IDM+ car following model is modified with additional controls to incorporate the current technologies of Advanced Driver Assistant Systems (ADAS). The results of this study showed that the proposed car following model can increase the throughput of the non-platoon lane from approximately 2000 vehicle per hour (vph) to 3400 vph while the platoon lanes each had an average throughput of 3500 vph. The merge model enabled higher merging throughput for the merge area compared to current day conditions and displayed the potential for improved traffic performance in a connected environment comprised of platoon and non-platoon lanes. The results of this research will help in the design and development of advanced systems for controlling on-ramp merge sections in the future with CAVs

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

A Methodology for the Design of Safety-Compliant and Secure Communication of Autonomous Vehicles

International audience; The automotive industry is increasing its effort towards scientific and technological innovations regarding autonomous vehicles. The expectation is a reduction of road accidents, which are too often caused by human errors. Moreover, technological solutions, such as connected autonomous vehicle platoons, are expected to help humans in emergency situations. In this context, safety and security issues do not yet have a satisfactory answer. In this paper, we address the domain of secure communication among vehicles - especially the issues related to authentication and authorization of inter-vehicular signals and services carrying safety commands. We propose a novel design methodology, where we take a contract-based approach for specifying safety, and combine it in the design flow with the use of the Arrowhead Framework to support security. Furthermore, we present the results through a demo, which employs model-based design for software implementation and the physical realization on autonomous model cars