Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving : deep learning approach

In today's modern electric vehicles, enhancing the safety-critical cyber-physical system CPS 's performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach. A deep neural network DNN is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.Published versio

Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted

Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment

Developing and Evaluating the driving and powertrain systems of automated and electrified vehicles (AEVs) for sustainable transport

In the transition towards sustainable transport, automated and electrified vehicles (AEVs) play a key role in overcoming challenges such as fuel consumption, emissions, safety, and congestion. The development and assessment of AEVs require bringing together insights from multiple disciplines such as vehicle studies to design and control AEVs and traffic flow studies to describe and evaluate their driving behaviours. This thesis, therefore, addresses the needs of automotive and civil engineers, and investigates three classes of problems: optimizing the driving and powertrain systems of AEVs, modelling their driving behaviours in microscopic traffic simulation, and evaluating their performance in real-world driving conditions. The first part of this thesis proposes Pareto-based multi-objective optimization (MOO) frameworks for the optimal sizing of powertrain components, e.g., battery and ultracapacitor, and for the integrated calibration of control systems including adaptive cruise control (ACC) and energy management strategy (EMS). We demonstrate that these frameworks can bring collective improvements in energy efficiency, greenhouse gas (GHG) emissions, ride comfort, safety, and cost-effectiveness. The second part of this thesis develops microscopic free-flow or car-following models for reproducing longitudinal driving behaviours of AEVs in traffic simulation, which can support the needs to predict the impact of AEVs on traffic flow and maximize their benefits to the road network. The proposed models can account for electrified vehicle dynamics, road geometric characteristics, and sensing/perception delay, which have significant effects on driving behaviours of AEVs but are largely ignored in traffic flow studies. Finally, we systematically evaluate the energy and safety performances of AEVs in real-world driving conditions. A series of vehicle platoon experiments are carried out on public roads and test tracks, to identify the difference in driving behaviours between ACC-equipped vehicles and human-driven vehicles (HDVs) and to examine the impact of ACC time-gap settings on energy consumption

Adaptive Regenerative Braking in Electric Vehicles

Elektrofahrzeuge fahren lokal emissionsfrei und tragen damit dazu bei, die Emissionen in Städten zu reduzieren. Zusätzlich, zeichnen sich Elektrofahrzeuge durch ein dynamisches Fahrverhalten aus. Nachteilig wirkt sich bei den meisten Elektrofahrzeugen, die geringe Reichweite auf die Akzeptanz bei Neuwagenkäufern aus. Eine der Maßnahmen zur Erhöhung der Reichweite von Elektrofahrzeuge ist das regenerative Bremsen. Hierbei wird die kinetische Energie des Fahrzeugs durch generatorisches Bremsen als elektrische Energie zurückgewonnen. Diese zurückgewonnene Energie erhöht die Reichweite des Autos. In dieser Dissertation, wird ein adaptives regeneratives Bremssystem vorgestellt. Dieses System wählt abhängig vom Fahrertyp und der aktuellen Verkehrssituation ein geeignetes regeneratives Bremsniveau aus. Um ein solches System zu realisieren, wurden Verfahren entwickelt, welche einerseits den Fahrertyp und andererseits die Fahrerintention durch Analyse des Fahrbetriebs ermitteln. Dazu wurde u.a. ein mehrdimensionales verstecktes Markov-Modell (MDHMM) entwickelt. Bei Verwendung des Fahrertyps und der Intention des Fahrers, kann so eine geeignete Bremsstufe ausgewählt werden, die die physikalische Begrenzung der Fahrzeugkomponenten berücksichtigt. Durch den Einsatz des entwickelten Systems, kann gezeigt werden, dass eine Erhöhung der Reichweite erreicht werden kann, ohne den Komfort des Fahrers zu beeinträchtigen

Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort

Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems