On Traffic Situation Predictions for Automated Driving of Long Vehicle Combinations

The introduction of longer vehicle combinations for road transports than are currently allowed is an important viable option for achieving the environmental goals on transported goods in Sweden and Europe by the year 2030. This thesis addresses how driver assistance functionality for high-speed manoeuvring can be designed and realized for prospective long vehicle combinations. The main focus is the derivation and usage of traffic situation predictions in order to provide driver support functionalities with a high driver acceptance. The traffic situation predictions are of a tactical character and include a time horizon of up to 10 s. Data collection of manual and automated driving with an A-double combination was carried out in a moving-base driving simulator. The driving scenario was comprised of a relatively curvy and hilly single-lane Swedish county road (180). The driving trajectories were analysed and complemented with results from optimization. Based on observations of utilized accelerations it was proposed that the combined steering and braking should prioritize a smooth and comfortable driving experience. It was hypothesized that high driver acceptance of driver assistance functionality including automated steering and propulsion/braking, can be realized by utilizing driver models inspired by human cognition as an integrated part in the generation of traffic situation predictions. A longitudinal and lateral driver model based on optic information was proposed for lane-change manoeuvring. The driver model was implemented in a real-time framework for automated driving of an A-double combination on a multiple lane one-way road. Simulations showed that the framework gave reasonable results for maintain lane and lane change manoeuvres at constant and varying longitudinal velocities

Operating cycle representations for road vehicles

This thesis discusses different ways to represent road transport operations mathematically. The intention is to make more realistic predictions of longitudinal performance measures for road vehicles, such as the CO2 emissions. It is argued that a driver and vehicle independent description of relevant transport operations increase the chance that a predicted measure later coincides with the actual measure from the vehicle in its real-world application. This allows for fair comparisons between vehicle designs and, by extension, effective product development. Three different levels of representation are introduced, each with its own purpose and application. The first representation, called the bird\u27s eye view, is a broad, high-level description with few details. It can be used to give a rough picture of the collection of all transport operations that a vehicle executes during its lifetime. It is primarily useful as a classification system to compare different applications and assess their similarity. The second representation, called the stochastic operating cycle (sOC) format, is a statistical, mid-level description with a moderate amount of detail. It can be used to give a comprehensive statistical picture of transport operations, either individually or as a collection. It is primarily useful to measure and reproduce variation in operating conditions, as it describes the physical properties of the road as stochastic processes subject to a hierarchical structure.The third representation, called the deterministic operating cycle (dOC) format, is a physical, low-level description with a great amount of detail. It describes individual operations and contains information about the road, the weather, the traffic and the mission. It is primarily useful as input to dynamic simulations of longitudinal vehicle dynamics.Furthermore, it is discussed how to build a modular, dynamic simulation model that can use data from the dOC format to predict energy usage. At the top level, the complete model has individual modules for the operating cycle, the driver and the vehicle. These share information only through the same interfaces as in reality but have no components in common otherwise and can therefore be modelled separately. Implementations are briefly presented for each module, after which the complete model is showcased in a numerical example.The thesis ends with a discussion, some conclusions, and an outlook on possible ways to continue

Transportation Mission Based Optimization of Heavy Vehicle Fleets including Propulsion Tailoring

Over decades freight vehicles were produced for a wide range of operational domains so that vehicle-manufacturers were not concerned much about the actual use-cases of the vehicles. Environmental issues, costumer expectations along with growing demand on freight transport created a competitive environment in providing better transportation solutions. In this thesis, it was proposed that freight vehicles can be designed more cost- and energy-efficiently targeting rather narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to deliver customized vehicles with tailored propulsion components that fit best given transportation missions and operational environment. Optimization-based design of vehicle components showed to be more effective considering optimization of transportation mission infrastructure simultaneously, including charging stations, routing and fleet composition and size, especially in case of electrified propulsion. It was observed that by implementing integrated vehicle hardware-transportation optimization, total cost of ownership can be reduced up to 35\%, in case of battery electric heavy vehicles.Furthermore, throughout thesis, the effect of propulsion system components size on optimal energy management strategy in hybrid heavy vehicles was studied; a methodology for solving fleet-size and mix-vehicle routing problem including enormous number of vehicle types were introduced; and the impact of Automated Driving Systems on electrified propulsion was presented

Decision-Making in Autonomous Driving using Reinforcement Learning

The main topic of this thesis is tactical decision-making for autonomous driving. An autonomous vehicle must be able to handle a diverse set of environments and traffic situations, which makes it hard to manually specify a suitable behavior for every possible scenario. Therefore, learning-based strategies are considered in this thesis, which introduces different approaches based on reinforcement learning (RL). A general decision-making agent, derived from the Deep Q-Network (DQN) algorithm, is proposed. With few modifications, this method can be applied to different driving environments, which is demonstrated for various simulated highway and intersection scenarios. A more sample efficient agent can be obtained by incorporating more domain knowledge, which is explored by combining planning and learning in the form of Monte Carlo tree search and RL. In different highway scenarios, the combined method outperforms using either a planning or a learning-based strategy separately, while requiring an order of magnitude fewer training samples than the DQN method. A drawback of many learning-based approaches is that they create black-box solutions, which do not indicate the confidence of the agent\u27s decisions. Therefore, the Ensemble Quantile Networks (EQN) method is introduced, which combines distributional RL with an ensemble approach, to provide an estimate of both the aleatoric and the epistemic uncertainty of each decision. The results show that the EQN method can balance risk and time efficiency in different occluded intersection scenarios, while also identifying situations that the agent has not been trained for. Thereby, the agent can avoid making unfounded, potentially dangerous, decisions outside of the training distribution. Finally, this thesis introduces a neural network architecture that is invariant to permutations of the order in which surrounding vehicles are listed. This architecture improves the sample efficiency of the agent by the factorial of the number of surrounding vehicles

En jämförande kostnadsstudie mellan ETT-fordonet och konventionella gruppbilar i Norrlands inland

Transportarbetet är en av de tyngsta kostnadsposterna för Svensk skogsbruk, dessutom står dessa transporter för en stor del av näringens koldioxidutsläpp och utsläpp av andra emissioner. Utsläpp som Sverige förbundit sig att minska med 20 % till år 2020. Sett till dessa problem och krav togs 2006 ett initiativ att söka utveckla transportarbetet genom att öka bruttovikterna per transport. Resultatet av detta initiativ blev ETT-fordonet, En Trave Till, en 30 meter lång fordonskombination med en totalvikt om 90 ton och en lastvikt om 66 ton. Syftet med denna studie var att jämföra ETT-fordonet mot en konventionell gruppbil i en specifik geografi i norra Norrlands inland med avseende på transportkostnaden för rundvirke. Studien genomfördes enligt följande: 1. En tidsstudie på momenten lastning och lossning genomfördes 2. Faktisk körhastighet ett gruppfordon på valda vägklasser undersöktes 3. Relevanta kostnadsparametrar såsom investerings-, försäkrings-, däckskostnader etcetera uppdaterades. Dessa data användes sedan för att uppdatera den modell som valts för att simulera kostnaderna: Gille kalkyl. Resultatet från studien visar att det är 18 % billigare per ton att transportera rundvirke med ETT-fordonet än ett konventionellt gruppfordon vid en transportsträcka om 100 km. Genomförd känslighetsanalys visar att körhastigheten på sämre vägklasser och möjligheten att nyttja full lastkapacitet är av stor betydelse för att kostnadsskillnaden till ETT-fordonets fördel skall bestå. Rekommendationen till uppdragsgivaren, Sveaskog, blir att det är värt att satsa på virkesfordon med ökad lastvikt om man säkerställer tänkta rutters vägstandard så full lastkapacitet kan nyttjas och körhastigheten ej nämnvärt sjunker.Transport is a major cost component for Swedish forestry. Transport also makes up a large part of the industry's carbon dioxide emissions, which Sweden is committed to reduce by 20% by 2020. These developments resulted in an initiative in 2006 to increase the gross vehicle weight for logging trucks. The result of this initiative was the ETT-vehicle (ETT: En Trave Till), a 30 meter long articulated vehicle with a gross vehicle weight of 90 tons and payload of 66 tons. The purpose of this study was to compare the transport costs for an ETT-vehicle to a conventional group logging truck in a specific geography in the inland area of northern Sweden. The study had three parts: 1. A supplementary time study on the loading and unloading of the ETT-vehicle. 2. A supplementary speed study of a group vehicles on selected road classes. 3. Updating of relevant cost parameters such as investment-, operator-, and fuel costs. The supplemented and updated data was then used to simulate transport costs in a currently available tariff-calculation model called Gille kalkyl. Results from the study showed that it was 18 % cheaper per ton to transport round wood with ETT-vehicle than a conventional group vehicle at a transport distance of 100 km. A sensitivity analysis showed that travel speed on lower road classes and the ability to utilize the full capacity are key parameters for the difference in cost between the two truck types. The profitability of a transition from conventional 60-ton group trucks to high capacity vehicles such as ETT is dependent on ensuring that prospective routes allow the assumed travel speeds and full load capacity

On Robust Steering Based Lateral Control of Longer and Heavier Commercial Vehicles

Rapid growth in the transportation of goods has led to raised concerns aboutenvironmental effects, road freight traffic, and increased infrastructure usage.The increasing cost of fuel, and issues with congestions and gas emissions,make longer and heavier commercial vehicles (LHCVs) an attractive alter-native to conventional heavy vehicles. However, one major issue concerningLHCVs is their potential impact on traffic safety. A typically dangerous be-haviour happens during evasive steering maneuvers, which causes amplifiedlateral motions in the towed units. These amplified motions can lead to thetowed units’ oscillation, large offtracking and, in a worst case scenario, causerollover.The main objective of this thesis is to develop robust steering-based con-trollers for improving the lateral performance of LHCVs at high speeds bysuppressing unwanted amplified motions in the towed units. Robust controlmethods aim to achieve an adequate level of robustness against model un-certainties and disturbances, while at the same time satisfying the desiredclosed-loop system performance specifications. The proposed robust controlsyntheses are formulated based on an H ∞ static output-feedback (SOFB)in which only one easily measurable state variable is required. The controlsynthesis problems are solved by using linear matrix inequality (LMI) op-timizations. As the measurement of the driver steering input is available,a combined version of SOFB and dynamic feed-forward (DFF) is also de-veloped and several techniques for designing DFF are proposed. The theo-retical contributions of this research mainly lie in the derivation of a novelLMI conditions for integral quadratic constraints on the states and also inthe derivation of a set of new LMI conditions for the DFF design method.From a practical point of view, the proposed controllers are simple and easyto implement, despite their theoretical complexity.The effectiveness of the designed controllers is verified through numericalsimulations performed on linear vehicle models as well as high-fidelity ve-hicle models. The verification results confirm a significant reduction in yawrate rearward amplification, lateral acceleration rearward amplification andhigh-speed transient off-tracking, thereby improving the lateral stability andperformance of the studied LHCVs

Computer simulation of offtracking of truck and trailer combinations using forest roads

Many of the analytical techniques that are currently available are not capable of analyzing the offtracking of truck and trailer combinations or the path of load overhangs through a set of curves that are commonly found on forest roads. Advances in the processing speed of microcomputers have made it possible to simulate how a truck and trailer combination negotiate a road. A microcomputer program, OFETRACK, was developed to simulate the offiracking of six different vehicle configurations. The program will enable road designers to predict offtracking through a complex set of road geometrics, including compound and reverse curves. The program will also analyze the path of load overhangs on a vehicle such as a yarder tower on a lowboy or rubber tired undercarriage, Test results of the program compare favorably with several field studies and a scale model simulator. Outputs of the computer simulation are in the form of road widths required left and right of centerline, total width, and curve widening, A plot of the paths of the wheels and load overhang can be viewed on the screen or sent to a drum plotte

A Driver Model Using Optic Information for Longitudinal and Lateral Control of a Long Vehicle Combination

High driver acceptance is believed to be an important aspect when introducing automated driving functionalities for prospective long vehicle combinations. The main hypothesis of this paper is that high driver acceptance can be realized by utilizing driver models inspired by human cognition as an integrated part of such functions. It is envisioned that the human driver will more easily understand, and trust, a system that behaves in a human-like manner. In the study of a combined retardation and lane-change scenario, a driver model based on optic information was used, together with a single track vehicle model, to control the steering and retardation of a simulated vehicle. The parameters of the driver model’s lateral behavior were estimated using driving data measured from an A-double combination during actual lane-changes. Numerical simulations showed that the driver model was able to generate safe and conservative deceleration and steering for the studied scenario. In future work for automated functionalities, the combined driver and vehicle model could be used when evaluating different tentative plans for lane changes, in real time

Performance characteristics for automated driving of long heavy vehicle combinations evaluated in motion simulator

This paper presents a driving simulator experiment in which manual and automated driving of a prospective long vehicle combination has been studied. Based on post analysis of manual and automated driving trajectories, characteristic measures reflecting the manual drivers behavior have been proposed. It was observed that the drivers had a round shape of the utilized accelerations while negotiating the curves. A similar shape was found when using an objective function which included minimizing the resultant jerk vector

A Lane-Change Gap Acceptance Scenario Developed for Heavy Vehicle Active Safety Assessment: A Driving Simulator Study

The aims of this study were to develop a lane-change scenario for driving simulators in order to analyse the characteristics of lane-change manoeuvres performed with heavy vehicles. The scenario was set up based on information from lane-change accidents and on-road lane-change observations. The gap acceptance scenario consisted of two consecutive lane changes were the intention was to study truck drivers’ accepted gap between two vehicles in the adjacent right lane, at the initiation of each lane change. An experiment was conducted with 18 truck drivers in a full-motion driving simulator with implemented high fidelity models of an 80tonnes and 32m long vehicle combination and a 40tonnes and 22m tractor semi-trailer. The results showed no statistically significant difference in the accepted gaps to the lead and lag vehicles in the target lane. For both heavy vehicles, the overall average lead gap and lag gap was estimated to 0.85s and 0.83s respectively, at the average velocity of 17.3m/s. The difference in lane-change duration for the two vehicles was statistically significant and estimated to an average of 8.7s for the tractor semi-trailer, and 10.5s for the A-double. The conclusion from the present study is that the drivers performed the lane changes equally well with the tractor semi-trailer and the long vehicle combination. There were no major differences between the manoeuvres other than the duration times, which can be justified by the difference in vehicle length. Future studies are able to use this scenario as a non-critical reference to more critical events in the development and assessment of active safety functionality and automated driving systems