Abstract

network-wide coordination system for heavy-duty vehicle platooning with the purpose of reducing fuel consumption is developed. Road freight is by far the dominating mode for overland transport with over 60 % modal share in the OECD countries and is thus critically important for the economy. Overcoming its strong dependency on fossil fuels and manual labor as well as handling rising congestion levels are therefore important societal challenges. Heavy-duty vehicle platooning is a promising near-term automated-driving technology. It combines vehicle-to-vehicle communication and on-board automation to slipstream in a safe manner, which can reduce fuel consumption by more than 10 %. However, in order to realize these benefits in practice, a strategy is needed to form platoons in an operational context. We propose a platoon coordination system that supports the process of automatically forming platoons over large geographic areas. We develop an architecture in which fleet management systems send start locations, destinations, and arrival deadlines to a platoon coordinator. By computing desirable speed profiles and by letting the vehicles' on-board systems track them, vehicles can meet en route and form platoons. Matching vehicles into platoons and deriving suitable speed profiles is treated as an optimization problem with the objective of maximizing the overall fuel savings under the constraint that vehicles arrive in time at their destinations. By updating the speed profiles and the platoon configurations based on real-time measurements of vehicle position and platoon state, the system can accommodate new vehicles joining on the fly. Using real-time measurements also makes the system resilient to disturbances and changing operating conditions. This thesis seeks to develop the theoretical foundations of such a system and evaluate its potential to improve transport efficiency.   We first explore the coordination of vehicle pairs. Fuel-optimal speed profiles are derived. The uncertainty arising from traffic is taken into account by modeling travel time distributions and considering the probability of two vehicles successfully merging. Building on this coordination algorithm for vehicle pairs, we derive algorithms for larger platoons and vehicle fleets. This results in an NP-hard combinatorial optimization problem. The problem is formulated as an integer program and results on the solution structure are derived. In order to handle realistic fleet sizes with thousands of vehicles and continental sized geographical areas under real-time operation, heuristic algorithms are developed. The speed profiles resulting from the combinatorial optimization are further improved using convex optimization. Moreover, we derive efficient algorithms to identify all pairs of vehicles that can platoon. Simulations demonstrate that the proposed algorithm is able to compute plans for thousands of vehicles. Coordinating approximately a tenth of Germany's heavy-duty vehicle traffic, platooning rates over 65 % can be achieved and fuel consumption can be reduced by over 5 %. The proposed system was implemented in a demonstrator system. This demonstrator system has been used in experiments on public roads that show the technical feasibility of en route platoon coordination. Ett nätverksomfattande system för att koordinera körning i lastbilskolonner i syfte att minska bränsleförbrukningen utvecklas i denna avhandling.Vägtransport är med marginal det mest dominerande sättet för landtransport med en andel över 60 % i OECD-länderna och är således avgörande för samhällsekonomin. Dess starka beroende av fossila bränslen och arbetskraft samt ökande trängsel är därför viktiga utmaningar för samhället. Vägtransport är på väg att genomgå grundläggande förändringar genom elektrifiering, kommunikation och automation. Lastbilskolonnkörning är en teknologi för att automatisera fordon, som är redo att lanseras inom en snar framtid. Kolonnkörning kan ge bränslebesparingar på över 10 % tack vare att automatisering och kommunikation mellan fordon möjliggör så kallad slipstreaming på ett säkert sätt. Det behövs dock en strategi för att sätta ihop lastbilskolonner för att kunna dra nytta av denna teknologi. Vi föreslår därför ett automatiserat koordineringssystem som stödjer processen att sätta ihop lastbilskolonner inom stora geografiska områden. Vi utvecklar en arkitektur där fleet management system skickar start- och målpunkter samt senaste ankomsttid till koordineringssystemet. Genom att beräkna passande hastighetsprofiler och genom att låta lastbilarnas färddatorer följa dem kan lastbilar mötas på vägen och bygga ihop kolonner. Problemet att matcha fordon till kolonner och att komma fram till passande hastighetsprofiler hanteras som ett optimeringsproblem med målet att maximera den totala bränslebesparingen. Genom att uppdatera lösningen baserade på realtidsmätningar av fordonspositioner och kolonntillstånd kan systemet hantera tillkommande lastbilar och kan vara robust gentemot störningar och förändrade operativa förutsättningar. Denna avhandling strävar efter att utveckla teoretiska förutsättningar för ett sådant system och att utvärdera dess potential att öka transportsystemets effektivitet. Vi börjar med att undersöka koordinering av fordonspar och härleder bränsleoptimala hastighetsprofiler. Den osäkerhet som beror på trafiken tas hänsyn till genom att modellera hastighetsfördelningar och genom att betrakta sannolikheten att två fordon träffas och kan påbörja kolonnkörning. Vi bygger på denna algoritm för fordonspar för att härleda algoritmer som kan hantera större kolonner och större antal lastbilar. Det resulterar i ett NP-svårt kombinatoriskt optimeringsproblem. Problemet formuleras som ett heltalsoptimieringsproblem och vi kommer fram till resultat som visar strukturen av lösningen. Resulterande hastighetsprofiler förbättras ytterligare med hjälp av konvex optimering. Förutom detta utvecklas effektiva algoritmer för att identifiera alla fordonspar som kan köra i kolonn. Simuleringar demonstrerar att den föreslagna algoritmen klarar att beräkna planer för tusentals lastbilar. Vi visar att en samording av c:a 10 % av Tysklands lastbilstrafik kan tillåta att mer än 65 % av den totala körsträckan körs i kolonn och att bränsleåtgången kan minskas med mer än 5 %. Det presenterade systemet implementerades i ett demonstratorsystem. Experiment med demonstratorn på allmän väg har visat att koordinering av kolonnkörning på vägen är tekniskt genomförbart. "p"QC 20180403

Abstract

The operation of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Tests conducted on flat roads have shown the potential of platooning to reduce the fuel consumption of about 10%. However, platoons are expected to operate on public highways with varying topography alongside other vehicles. Due to the large mass and limited engine power of heavy-duty vehicles, road slopes have a significant impact on feasible and optimal speed profiles. For single vehicles, experiments have shown that optimizing the speed according to the road profile resulted in fuel saving of up to 3.5%. The use of such a look-ahead control framework is expected to lead to large benefits also for platooning. This thesis presents the design of safe and fuel-efficient control of heavy-duty vehicle platoons driving on realistic road profiles. The scenario where the platooning vehicles cooperate to optimize their overall fuel-efficiency is studied together with the scenario where the vehicles do not explicitly cooperate. First, we propose a control architecture that splits the cooperative platooning control problem into two layers. The top layer computes a reference speed profile that ensures fuel-efficient operation of the entire platoon based on dynamic programming. The bottom layer relies on model predictive control to safely track the reference speed. Simulations show the ability of the proposed controller to save up to 12% of fuel for following vehicles compared to existing platoon controllers and to safely react to emergency braking of the leading vehicle. Second, we propose a gear management layer that fits in the cooperative platooning control architecture and explicitly takes the gear selection into account. The underlying optimal control problem aims at minimizing the vehicle fuel consumption and the reference tracking deviations. Simulations indicate how this formulation outperforms existing alternatives, both in terms of fuel-efficiency and tracking error. Third, we address non-cooperative platooning by proposing a vehicle-following controller suitable for fuel-efficient control of heavy-duty vehicles. The proposed controller explores both the benefits given by the short inter-vehicular distance and those given by pulse-and-glide, i.e., alternating traction and coasting phases. A simulation study suggests fuel saving of up to 18% compared to the single vehicle case, and up to 7% compared to when a constant-distance vehicle-following controller is used. Last, we propose a vehicle-following controller aimed at exploiting long preview of the preceding vehicle trajectory by directly manipulating the inputs of low-level vehicle controllers. This is achieved through a model predictive controller that uses a short prediction horizon and includes a terminal state set that incorporates preview information about the preceding vehicle. Experiments indicate the ability of the controller to avoid unnecessary braking, while simulations show behavior similar to the optimal control behavior. "p"QC 20180416

Abstract

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability. Document type: Article