Supporting energy-efficient driving is essential for sustainable mobility in electric vehicles (EVs), where operational eco-driving (or ecodriving) can significantly reduce energy consumption. This study investigates the effects of two types of ecodriving displays—an Instantaneous Consumption Display (ICD) and an Optimal Speed Display (OSD)—on energy consumption and energy-related situation awareness (Energy Dynamics Awareness, EnDynA). In the EcoSimLab EV simulator, N = 94 participants were assigned to the ICD, OSD, or a control condition and completed multiple driving trials with varying situation complexity. Improvement, defined as the difference between the display and baseline conditions, in EnDynA and eco-driving performance, was greater in the display groups than in the control. The OSD, providing predictive speed recommendations by dynamic programming, was particularly effective in more complex driving scenarios. The ICD, offering real-time consumption feedback, facilitated learning in simpler scenarios. Findings underscore the importance of adaptive ecodriving support systems, balancing real-time feedback with predictive guidance.

Model Predictive Control (MPC) has emerged as a pivotal technology for optimizing control tasks in autonomous driving, particularly within Adaptive Cruise Control (ACC) systems. However, the manual tuning of MPC cost function weights and prediction horizons remains a significant challenge. In this paper, we introduce a novel framework that combines Deep Reinforcement Learning (DRL) with MFC to dynamically tune both the weight parameters and prediction horizon in real time. This approach, referred to as the Weights and Prediction Horizon Varying MPC (W-PH-MPC), overcomes traditional MPC limitations by utilizing proximal Policy optimisation and Deep Deterministic Policy Gradient (DDPG) algorithms to adjust control parameters. We evaluate the effectiveness of our approach through simulations in vehicle-tracking scenarios. Simulation results show that the adaptive MPC-RL controller achieves better tracking performance, without compromising power consumption, and lowers longitudinal jerk compared to a fixed-parameter MPC baseline, resulting in smoother and more efficient vehicle behavior.

Activity recognition is one of the major tasks in computer science and engineering that aims to recognize and understand the actions of one or more agents from a series of observations. It assists different sectors in ensuring safety in human-machine interaction. This paper investigates and tests an encoder classifier based on an LSTM model with various attention mechanism structures over a synthetic time-series motion dataset generated with the NVIDIA Isaac simulator. The simulator simulates the motion of an excavator as an example of a complex kinematic system in a construction environment, gathering data from position sensors connected to the excavator’s main joints. The results show that fewer sensors can be used for certain types of motion classification with a large accuracy range between 66.7% and 83.3%. The encoder-LSTM model with scaled dot-product attention gives the most accurate results compared to other attention mechanism types, with around 33.4% when using data from only two sensors and around 22.2% for the four main sensors. These results are in comparison to models that used other attention mechanisms.

Working with construction machinery necessitates that workers remain attentive to effectively survey their surroundings. The complex environment, blind spots, and limited visibility while simultaneously tackling complex tasks create hazardous conditions. Camera systems providing a bird view help monitoring the machine’s surroundings, but still require active monitoring while working. In this paper, we propose extending bird-view systems with object detection. We investigate and evaluate two different approaches: Detecting people in four fisheye camera images and transforming the obtained bounding boxes into a bird view, and directly detecting people in bird view. Our models, based on the YOLOv5 and YOLOv8 architectures, were trained and validated on custom datasets with 3,302 fisheye images and 1,343 bird-view images. The models achieved [email protected] scores of 0.514 (fisheye) and 0.546 (bird view) and run at 25 frames per second on an NVIDIA Jetson Nano edge GPU. Our trained models for the detection utilize the YOLOv5 and YOLOv8 architecture and run inference with 25 frames per second on an NVIDIA Jetson Nano edge GPU. The first approach also addresses objects disappearing in stitching areas - characteristic of bird-view projection errors - by displaying bounding boxes even if they are invisible in bird view.

Although interpretable controllers are widely used in vehicle systems, they have received limited attention as models of human driving behavior. This study explores whether two such control strategies, a preview-augmented Proportional-Integral-Derivative (PID) controller and a constraint-based Model Predictive Control (MPC) framework, can model human longitudinal driving behavior when adapted via Bayesian optimization. A structured dataset of human driving behavior, recorded with participants in a driving simulator, was used to train and evaluate both controllers across acceleration, deceleration, and cruising scenarios. MPC achieved lower overall deviation and more consistent performance across trials. These findings highlight the potential of combining interpretable control architectures with data-driven parameter adaptation to model human driving behavior effectively.

Driving simulators often lack fundamental components needed for accurate simulation of energy dynamics. We introduce EcoSimLab, a comprehensive electric vehicle driving simulation framework consisting of (1) a simulation of electric vehicle energy dynamics, (2) an optimization-based approach of structuring eco-driving behaviors, (3) a synthetic driver module as versatile benchmark model to analyze human behavior. Guided by fundamentals of energy modeling and considerations on human action regulation, we further present the development of the EcoDrivingTestPark, an exemplary set of energy-relevant scenarios to enable the analysis of individual differences in eco-driving and intervention effects (e.g., HMIs). To generate a first characterization of driving behavior, we conducted two empirical studies with human ( <?TeX $N_\text{S1}~=~31$?> Math 1 , <?TeX $N_\text{S2a}~=~41$?> Math 2 ) and synthetic drivers ( <?TeX $N_\text{S2b}~=~3$?> Math 3 ). Results indicate substantial variations in driver behavior and considerable challenges for human drivers to achieve synthetic driver performance. Implications for augmenting human action regulation in eco-driving are discussed.

The development of 5G enables communication systems to satisfy heterogeneous service requirements of novel applications. For instance, ultra-reliable low latency communication (uRLLC) is applicable for many safety-critical and latency-sensitive scenarios. Many research papers aim to convert the stringent reliability and latency factors to a static data rate requirement. However, in most industrial scenarios, the communication traffic presents short-term/long-term dependency, burst, and non-stationary characteristics. This makes it more challenging to obtain a tight upper bound for the rate requirement of uRLLC. In this work, we introduce a novel solution based on decentralized model predictive control (MPC), where the dynamic incoming communication traffic and the users’ quality of service (QoS) requirements are reformulated into an up-to-date data rate constraint. Under such assumptions, we consider a use case of the resource allocation problem for a single uRLLC network slice. The allocation task is solved by the successive convex approximation (SCA) algorithm for a more in-depth analysis. The simulation results show that the proposed algorithm can deal with non-stationary communication traffic in real-time, as well as provide good performance with guaranteed delay and reliability requirements.

Backtracking is generally used for interior-point methods (IPMs) to keep some optimization parameters within a defined boundary. The idea is to reduce the step size such that the optimality conditions are fulfilled. However, backtracking might impede progress toward the optimal point, requiring longer solving time and higher iteration numbers, which are undesirable in real-time applications. In this paper, we present a novel algorithm based on an interior-point method that, in addition to accepting infeasible start guess points, liberates the Lagrange multiplier associated with the inequality constraints from backtracking. Accordingly, a new strategy for updating the weighting factor of the barrier function is proposed. The performance of this new IPM is benchmarked with well-known optimization solvers for solving both linearly and quadratically constrained quadratic programming (QP) problems, which are formulated based on an application of model predictive control (MPC) in the automotive industry. Furthermore, the new IPM has been implemented on embedded hardware and validated in an experimental real vehicle. The testing results show that the new IPM takes considerably fewer iterations and computational time to solve QP problems than other tested solvers.

This paper presents a novel formulation for scheduling the charging and discharging of electrical vehicles (EVs) in microgrids, considering a conditional minimum energy limit. The idea of utilizing EVs as storage units through vehicle-to-grid technology helps to stabilize the microgrid, especially with the intermittent power production of renewable energy resources. However, discharging the EV battery to very low levels can have negative impacts on its health and may not be satisfactory for the EV owner. Optimization-based methods show promise for scheduling, but the minimum energy constraint can make the problem infeasible when the initial EV energy is below the minimum limit. In this paper, we discuss existing approaches and their drawbacks in handling this issue. To address these drawbacks, we propose a novel approach that models the battery as two separate energy reservoirs. A case study is presented to demonstrate the effectiveness of our proposed method. Noteworthy, this concept can be applied not only to EV batteries but also to stationary batteries within microgrids.

This work proposes an eco-driving assistance system (EDAS) based on model predictive control (MPC) with a primary objective to improve the driver’s driving style in an energy-efficient manner. To improve the efficiency of an EDAS, a learning-based approach to model the driver behavior from an urban driving data collected using a dynamic driving simulator is presented. To cluster the driving data of thirty-four participants, unsupervised learning techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used. Furthermore, to predict the driver speed error while tracking an advisory speed, both stochastic and determinstic models, namely Stochastic Volatility (SV) and Gated Recurrent Unit (GRU) respectively, are trained. Six new drivers evaluated the proposed concept, whose driving style is classified using a trained temporal convolution network (TCN). Using the predicted driver speed error, the eco-driving advisory speed is compensated and provided as a feedback to the driver via a human-machine interface (HMI). The results reveal that the deterministic model has been able to achieve higher prediction accuracy as compared to the stochastic model. Furthermore, the results also suggest that the drivers using EDAS with driver error compensation have been able to perform better advisory speed tracking and achieve improved energy savings.

In this paper an ecological adaptive cruise controller to reduce the fuel consumption and ensure the safe inter-vehicle distance for vehicles with step-gear transmissions is presented. An optimal control strategy using reinforcement learning with a novel actor-gear-critic architecture is proposed to obtain the continuous traction force trajectory and the discrete gear shift schedule. The traction force is determined from an actor network to maintain a desired inter-vehicle distance which improves the driving safety in a car-following process. The gear shift schedule is derived from a gear network to reduce the fuel consumption. The control strategy is model-free and allows continuous online learning for different driving situations without look-ahead velocity information. Particularly the nonlinear vehicle dynamics, the nonlinear transmission efficiency map for different gear ratios, and the nonlinear fuel consumption map are learned for fuel consumption reduction. The proposed controller is evaluated for different driving scenarios to demonstrate its robustness. Furthermore simulation comparisons for different gear shift schedules and velocity trajectories are given underling the advantages in terms of fuel economy and driving safety.

The design of effective energy interfaces for electric vehicles needs an integrated perspective on the technical and psychological factors that together establish real-world vehicle energy efficiency. The objective of the present research was to provide a transdisciplinary synthesis of key factors for the design of energy interfaces for battery electric vehicles (BEVs) that effectively support drivers in their eco-driving efforts. While previous research tends to concentrate on the (visual) representation of common energy efficiency measures, we focus on the design of action-integrated metrics and indicators for vehicle energy efficiency that account for the perceptual capacities and bounded rationality of drivers. Based on this rationale, we propose energy interface examples for the most basic driving maneuvers (acceleration, constant driving, deceleration) and discuss challenges and opportunities of these design solutions.

On vehicles there exists a close coupling between brake and suspension dynamics, making semi-active damper control a promising way for brake maneuver optimization, which has been widely researched in the automotive and motorcycle field. Experimental data of bicycle dynamics analyzed in this paper show substantial differences to classical vehicle dynamics. By first-principle control-oriented modeling and full vehicle nonlinear multibody simulation it is shown that this can be traced back to two phenomena: the dynamic rider response and fork bending. These are very general effects, but crucial for vehicle dynamics control on bicycles, one of the most widely used means of transportation.

Suspension systems on bicycles have a tendency to severe brake-induced dive-in, caused by the small wheelbase in combination with a high center of gravity. Semi-active dampers allow the implementation of anti-dive functionality, preventing this behavior. Experimental analysis has shown that this yields significant advantages during brake control on level surfaces. In the presence of additional road excitation, however, a strong conflict arises. A specific test case is a bump occurring while braking, when the damping is set to the hardest value in order to mitigate dive-in. A simulative analysis illustrates that especially the dynamic wheel load is affected, which during braking is safety critical. By simulation and experimental implementation it is shown that using a simple semi-active control rule a decent trade-off can be found. Finally, the influence of the actuator response time is evaluated.

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.

Decentralized control approaches arise as promising solutions to cope with the high complexity of interconnected networks such as in the traffic domain. Leaving the control of traffic flow in the hands of distributed traffic nodes opens up the potential for a more efficient network utilization and a higher adaptability to changing traffic conditions. However, some control approaches based on decentralized information handling will introduce information delays which can result in suboptimal system performance and unintended effects such as oscillating traffic flows and traffic congestions. This paper compares the performance of a robust PI control approach without and with Smith predictor for an automated traffic control node with delayed traffic information. Results are validated with a realistic traffic simulation tool.

This paper presents an energy management system for smart grids with electric vehicles based on hierarchical model predictive control (HiMPC). The energy management system realizes load-frequency control (LFC), an economic operation and an electric vehicle integration into the smart grid. The main component is the HiMPC, which allows covering different time scales, regarding constraints (e.g. power ratings) and predictions (e.g. on renewable generation), as well as rejecting disturbances (e.g. due to fluctuating renewable generation) based on a systematic model- and optimization-based design. For the electric vehicle integration, an aggregator is proposed as link between HiMPC and individual vehicle. The aggregator in particular provides predictions to the HiMPC on the availability of electric vehicles for LFC based on the current mobility demand and the statistical mobility behavior of the vehicle users. Throughout the paper, the energy management system is evaluated for the smart grid of an intermediate city.