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Jonas Fredriksson - IEEE Xplore Author Profile

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Coordinating the motion profiles of fully automated vehicles in confined sites presents a challenge, particularly in avoiding conflicts in MUTually EXclusive (MUTEX) zones like intersections, merge-splits, and narrow roads. Prior methods have utilized optimization-based formulations, where the scheduling (order) of vehicles in MUTEX zones is found using a heuristic, followed by solving a nonlinear...Show More
Detailed longitudinal dynamics simulations may be used to predict the energy performance of road vehicles. However, including uncertainty in the operating conditions often implies high computational costs. Model-based formulations, in conjunction with statistical methods, may obviate this limitation by directly accounting for stochasticity, thus eliminating the need for simulating large population...Show More
In this paper, we present a framework for combined path and motion trajectory planning for the purpose of coordinating fully automated vehicles in confined sites. The path planning component utilizes a Monte-Carlo tree search approach for computing the vehicle paths and the motion trajectory component utilizes a two-stage optimization-based algorithm that optimizes the state and input trajectories...Show More
Articulated Heavy Vehicles (AHVs) play a crucial role in today’s transportation, offering significant commercial and environmental advantages. However, challenges like jackknifing and trailer swing in AHVs highlight the need for focused research. This paper introduces innovative yaw stability algorithms designed to tackle these concerns, employing advanced control allocation techniques, including ...Show More
This article presents a two-stage optimization-based methodology to control the motion of automated vehicles (AVs) in confined sites where human-driven vehicles (HDVs) are also present. A centralized high-level controller computes the state and input trajectories of the AVs based on the estimated driving behavior of the HDVs such that occupancy conflicts are avoided in cross-intersections, narrow ...Show More
In this paper, we present a high-level optimization-based control strategy for the coordination of electric automated vehicles (AVs) in confined sites. A centralized controller optimizes the state and input trajectories of all vehicles in the site such that collisions are avoided in cross-intersections, narrow roads, merge crossings, and charging stations, while also considering the charging proce...Show More
In this paper, we present an optimization-based control strategy for coordinating multiple electric automated vehicles (AVs) in confined sites. The approach focuses on obtaining and keeping energy-efficient driving profiles for the AVs while avoiding collisions in cross-intersections, narrow roads, and merge crossings. Specifically, the approach is composed of two optimization-based components. Th...Show More
A lane keeping assist system uses sensor and environmental information to automatically steer the vehicle, whenever necessary, to keep it within the lanes. As the system overrides the driver, it is important that automatic interventions are only used when the driver is unaware of the traffic situation, i.e., in cases of unintentional lane departures. Hence, one of the major challenges for such sys...Show More
This article studies optimal thermal management and charging of a battery electric vehicle driving over long-distance trips. The focus is on the potential benefits of including a heat pump in the thermal management system for waste heat recovery, and charging point planning, in a way to achieve optimality in time, energy, or their trade-off. An optimal control problem is formulated, in which the o...Show More
Advanced driver assistance systems typically support the driver in cases where the driver is likely to fail the driving task. The challenge, from a system perspective, is to accurately detect those cases. Recently, machine learning-based prediction models that are able to estimate the prediction uncertainty in real-time have successfully been introduced for this purpose. However, very little effor...Show More
This article addresses optimal battery thermal management, charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimization problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics...Show More
We investigate the problem of coordinating multiple automated vehicles (AVs) in confined areas. This problem can be formulated as an optimal control problem (OCP) where the motion of the AVs is optimized such that collisions are avoided in cross-intersections, merge crossings, and narrow roads. The problem is combinatorial and solving it to optimality is prohibitively difficult for all but trivial...Show More
Confined areas present an opportunity for early deployment of autonomous vehicles (AV) due to the absence of non-controlled traffic participants. In this paper, we present an approach for coordination of multiple AVs in confined sites. The method computes speed-profiles for the AVs such that collisions are avoided in cross-intersection and merge crossings. Specifically, this is done through the so...Show More
Neural networks are currently suggested to be implemented in several different driving functions of autonomous vehicles. While showing promising results the drawback lies in the difficulty of safety verification and ensuring operation as intended. The aim of this paper is to increase safety when using neural networks, by proposing a monitoring framework based on novelty estimation of incoming driv...Show More
Advanced driver assistance systems have been an active research topic for decades, for which many approaches have been developed not only to reduce the number of traffic accidents but also to increase the driver’s comfort. Among the many different solutions proposed, learning-based prediction approaches have gained considerable attention in recent years. Within this scope, this work focuses on the...Show More
This paper presents a computationally efficient algorithm for eco-driving along horizons of over 100 km. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity (kinetic energy) and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic e...Show More
In this paper, we consider the computation of robust N-step backward reachable sets for state- and input con- strained linear time-varying systems with additive uncertainty. We propose a method to compute a linear, time-varying control law that maximizes the volume of the robust N-step reachable set for the closed-loop system. The proposed method is an extension of recent developments and involves...Show More
This paper presents a method for accelerated evaluation of an automated driving function using the subset simulation method. The focus of the paper is to investigate how the evaluation is affected by the choice of metric that is used to steer the subset simulation towards failure. This is done by comparing the use of some common threat assessment metrics and see how close the estimated failure rat...Show More
This paper proposes a robust load-dependent controller synthesis to improve the lateral stability and performance of an A-double vehicle (tractor-semitrailer-dolly-semitrailer) at high speeds and consequently road safety by active steering of the dolly unit. The mass of the semitrailers resides in an interval between empty and fully-loaded scenarios and can be measured online. The yaw moments of i...Show More
This paper addresses eco-driving of an electric vehicle driving in a hilly terrain under stochastic wind speed uncertainty. The eco-driving problem has been formulated as an optimisation problem, subject to road and traffic information. To enhance the computational efficiency, the dimension of the formulated problem has been reduced by appending trip time dynamics to the problem objective, which i...Show More
In this paper, a path prediction model is presented and used to detect unintended lane departures caused by erroneous driving behaviors. The prediction model is inspired by the concept of a linear vector autoregressive model that is commonly used for multiple time series analysis. The original concept is extended to allow sparse historic sampling, which is shown to reduce the computational complex...Show More
This paper addresses optimising a trans-port mission by controlling the mission start time and velocity profile of an electric vehicle (EV) driving in a hilly terrain, subject to legal and dynamic speed limits imposed by traffic congestion. To this end, a nonlinear program (NLP) is formulated, where the mission start time is allowed to vary within an interval and final time is kept free. The goal ...Show More
Safe motion planning for automated vehicles requires that a collision-free trajectory can be guaranteed. For that purpose, we propose a monitoring concept that would ensure safe vehicle states. Determining these safe states, however, is usually a computationally demanding task. To alleviate the computational demand, we investigate the possibility to compute the safe sets offline. To achieve this, ...Show More
This work considers the problem of position and position-uncertainty estimation for atonomous vehicles during power black-out, where it cannot be assumed that any position data is accessible. To tackle this problem, the position estimation will instead be performed using power separated and independent measurement devices, including one inertial 6 Degrees of Freedom (DOF) measurement unit, four an...Show More
Lane departures, where the vehicle leaves the lane due to driver inattention, drowsiness, or incorrect situation assessment, are one of the most serious accident and fatality prone scenarios. To further improve traffic safety, we are asking the question: How much can a neural network approach improve the reliability of lane departure predictions compared to traditional model-based methods? Our res...Show More