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

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