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

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

A parametric approach for evaluating the stability of agricultural tractors using implements during side-slope activities

A methodological approach for evaluating a priori the stability of agricultural vehicles equipped with different mounted implements and operating on sloping hillsides is shown here. It uses a Matlab simulator in its first phase and, subsequently, the Response Surface Modelling (RSM) to evaluate the coefficients of a set of regression equations able to account for the Type-I and Type-II stability of the whole vehicle (tractor + implement with known dimensions and mass). The regression equations can give reliable punctual numeric estimations of the minimum value of the Roll Stability Index (RSI) and can verify the existence of a Type-I equilibrium without the need of using the simulator or knowing any detail about the model implemented in it. The same equations can also be used to generate many intuitive graphs (\u201cequilibrium maps\u201d) useful to verify quickly the possible overturning of the vehicle. A case-study concerning a 4-wheel drive articulated tractor is then presented to show the potential of the approach and how using its tools. The tractor has been studied in three scenarios, differing on where the implement has to be connected to the tractor (1: frontally; 2: frontally-laterally; 3: in the back). After performing a series of simulations, a set of polynomial models (with 6 independent variables) has been created and verified. Then, these models were used, together with the related equilibrium maps, to predict the stability of 8 implements for scenario 1, 7 implements for scenario 2, and 3 implements for scenario 3, evidencing in particular the danger of using a lateral shredder with a mass greater than 245 kg. The proposed approach and its main outcomes (i.e., the regression equations and the equilibrium maps) can give an effective contribution to the preventive safety of the tractor driver, so it could be useful to integrate it in the homologation procedures for every agricultural vehicle and to include the resulting documentation within the tractor logbook

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed

Trailer Sway Control Using an Active Hitch

The handling and yaw stability characteristics of passenger vehicles are drastically changed when towing a trailer, which can lead to unsafe oscillations in the trailer yaw, known as trailer sway. This thesis examines the feasibility of using lateral articulation of the hitch ball to reduce sway behavior in passenger-sized tractor-trailer configurations. An articulating hitch ball design has the advantage of not being dependent on the trailer being towed, providing stability improvements to the wide variety of trailers that a passenger vehicle may tow over its life cycle. Changes in the lateral position of the hitch relative to the tractor create dynamic changes to the heading angle of the trailer relative to the tractor, which act as compensating steering inputs into the system. To examine the effectiveness of this method, a linear handling model was developed to predict the system response with different trailer configurations and feedback methods. This model was simulated with various feedback controllers, and the modeling was validated using a model constructed in MapleSim, a high-fidelity multibody simulation tool. After establishing the required performance characteristics of the active hitch, a prototype was designed, manufactured, and tested in a full scale tractor-trailer combination. The modeling techniques showed good agreement with the physical testing, where the control design of proportional feedback on the trailer articulation angle provided improved yaw stability across many trailer configurations. The simple controller design is adaptable to driving conditions and requires minimal measurements of vehicle states. The performance of the active hitch prototype is best shown in a response to a steering impulse at 65km/h, where a highly unstable trailer causes steady state oscillation without control, and settles in under 4 seconds with control active

Trailer Sway Control Using an Active Hitch

The handling and yaw stability characteristics of passenger vehicles are drastically changed when towing a trailer, which can lead to unsafe oscillations in the trailer yaw, known as trailer sway. This thesis examines the feasibility of using lateral articulation of the hitch ball to reduce sway behavior in passenger-sized tractor-trailer configurations. An articulating hitch ball design has the advantage of not being dependent on the trailer being towed, providing stability improvements to the wide variety of trailers that a passenger vehicle may tow over its life cycle. Changes in the lateral position of the hitch relative to the tractor create dynamic changes to the heading angle of the trailer relative to the tractor, which act as compensating steering inputs into the system. To examine the effectiveness of this method, a linear handling model was developed to predict the system response with different trailer configurations and feedback methods. This model was simulated with various feedback controllers, and the modeling was validated using a model constructed in MapleSim, a high-fidelity multibody simulation tool. After establishing the required performance characteristics of the active hitch, a prototype was designed, manufactured, and tested in a full scale tractor-trailer combination. The modeling techniques showed good agreement with the physical testing, where the control design of proportional feedback on the trailer articulation angle provided improved yaw stability across many trailer configurations. The simple controller design is adaptable to driving conditions and requires minimal measurements of vehicle states. The performance of the active hitch prototype is best shown in a response to a steering impulse at 65km/h, where a highly unstable trailer causes steady state oscillation without control, and settles in under 4 seconds with control active

Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks

Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on