Kuo-Yun Liang

The Development of a Cooperative Heavy-Duty Vehicle for the GCDC 2011: Team Scoop

The first edition of the Grand Cooperative Driving Challenge (GCDC) was held in the Netherlands in May 2011. Nine international teams competed in urban and highway platooning scenarios with prototype vehicles using cooperative adaptive cruise control. Team Scoop, a collaboration between KTH Royal Institute of Technology, Stockholm, Sweden, and Scania CV AB, Södertälje, Sweden, participated at the GCDC with a Scania R-series tractor unit. This paper describes the development and design of Team Scoops prototype system for the GCDC. In particular, we present considerations with regard to the system architecture, state estimation and sensor fusion, and the design and implementation of control algorithms, as well as implementation issues with regard to the wireless communication. The purpose of the paper is to give a broad overview of the different components that are needed to develop a cooperative driving system: from architectural design, workflow, and functional requirement descriptions to the specific implementation of algorithms for state estimation and control. The approach is more pragmatic than scientific; it collects a number of existing technologies and gives an implementation-oriented view of a cooperative vehicle. The main conclusion is that it is possible, with a modest effort, to design and implement a system that can function well in cooperation with other vehicles in realistic traffic scenarios.

A Distributed Framework for Coordinated Heavy-Duty Vehicle Platooning

Heavy-duty vehicles (HDVs) traveling in single file with small intervehicle distances experience reduced aerodynamic drag and, therefore, have improved fuel economy. In this paper, we attempt to maximize the amount of fuel saved by coordinating platoon formation using a distributed network of controllers. These virtual controllers, placed at major intersections in a road network, help coordinate the velocity of approaching vehicles so they arrive at the junction simultaneously and can therefore platoon. This control is initiated only if the cost of forming the platoon is smaller than the savings incurred from platooning. In a large-scale simulation of the German Autobahn network, we observe that savings surpassing 5% when only a few thousand vehicles participate in the system. These results are corroborated by an analysis of real-world HDV data that show significant platooning opportunities currently exist, suggesting that a slightly invasive network of distributed controllers, such as the one proposed in this paper, can yield considerable savings.

When is it Fuel Efficient for a Heavy Duty Vehicle to Catch Up With a Platoon

Vehicle platooning has in recent years become an important research eld for thevehicle industry. By establishing a platoon of heavy duty vehicles, the fuel consumption can bereduced for the followe ...

Cyber–Physical Control of Road Freight Transport

Freight transportation is of outmost importance in our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of the total energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber–physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.

Heavy-Duty Vehicle Platoon Formation for Fuel Efficiency

Heavy-duty vehicles driving close behind each other, also known as platooning, experience a reduced aerodynamic drag, which reduces the overall fuel consumption up to 20% for the trailing vehicle. However, due to each vehicle being assigned with different transport missions (with different origins, destinations, and delivery times), platoons should be formed, split, and merged along the highways, and vehicles have to drive solo sometimes. In this paper, we study how two or more scattered vehicles can cooperate to form platoons in a fuel-efficient manner. We show that when forming platoons on the fly on the same route and not considering rerouting, the road topography has a negligible effect on the coordination decision. With this, we then formulate an optimization problem when coordinating two vehicles to form a platoon. We propose a coordination algorithm to form platoons of several vehicles that coordinates neighboring vehicles pairwise. Through a simulation study with detailed vehicle models and real road topography, it is shown that our approach yields significant fuel savings.

Coordinated route optimization for heavy-duty vehicle platoons

Heavy-duty vehicles traveling in platoons consume fuel at a reduced rate. In this paper, we attempt to maximize this benefit by introducing local controllers throughout a road network to facilitate platoon formations with minimal information. By knowing a vehicles position, speed, and destination, the local controller can quickly decide how its speed should be possibly adjusted to platoon with others in the near future. We solve this optimal control and routing problem exactly for small numbers of vehicles, and present a fast heuristic algorithm for real-time use. We then implement such a distributed control system through a large-scale simulation of the German autobahn road network containing thousands of vehicles. The simulation shows fuel savings from 1-9%, with savings exceeding 5% when only a few thousand vehicles participate in the system. We assume no vehicles will travel more than the time required for their shortest paths for the majority of the paper. We conclude the results by analyzing how a relaxation of this assumption can further reduce fuel use.

Fuel-Saving Potentials of Platooning Evaluated through Sparse Heavy-Duty Vehicle Position Data

Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.

The impact of heterogeneity and order in heavy duty vehicle platooning networks (poster)

It is formally known that by establishing a heavy duty vehicle platoon, the fuel consumption is reduced for the follower vehicle due to the lower air drag. However, it is not clear how the platoon should be formed with respect to the heavy duty vehicle properties. String stability is a well discussed issue in vehicle platooning. However, each vehicles properties have to be taken into consideration when analyzing the platoon system. In this paper, we analyze one property of heavy duty vehicles — the mass. The results show that the robustness is influenced by the order and physical characteristics of the vehicles in the platoon. When utilizing identical PID controllers for all vehicles in the platoon, it is better to arrange the heaviest vehicle first with decreasing mass order when considering the platoon behavior. However, in reality it is difficult to start rearranging a platoon in the middle of a highway and it would also require V2V-communication. A controller is often optimized for a particular configuration set that can cause slinky effects to the platoon. Therefore, a mass-dependent PID controller is introduced to establish a better platoon behavior for heavy duty vehicles. The results show no slinky effects regardless of the vehicle order in the platoon.

The influence of traffic on heavy-duty vehicle platoon formation

Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.

Cooperation patterns between fleet owners for transport assignments

We study cooperation patterns between the heavy-duty vehicle fleet owners to reduce their costs, improve their fuel efficiency, and decrease their emissions. We consider a distributed cooperation pattern in which the fleet owners can communicate directly with each other to form alliances. A centralized cooperation pattern is studied in which the fleet owners pay to subscribe to a third-party service provider that pairs their vehicles for cooperation. The effects of various pricing strategies on the behaviour of fleet owners and their inclusiveness are analyzed. It is shown that the fleet size has an essential role.