Advanced Driver Assistance Systems (ADAS) are technologies that automate, facilitate, and improve the vehicle’s systems. Indeed, these systems directly interfere with braking, acceleration, and drivability of driving operations. Thus, the use of ADAS directly reflects the psychology behind driving a vehicle, which can have an automation level that varies from fully manual (Level 0) to fully autonomous (Level 5). Even though ADAS technologies provide safer driving, it is still a challenge to understand the complexity of human factors that influence and interact with these new technologies. Also, there has been limited exploration of the correlation between the physical and cognitive driver reactions and the characteristics of Brazilian roads and traffic. Therefore, the present work sought to establish a preliminary investigation into a method for evaluating the driving response profile under the influence of ADAS technologies, such as Lane Centering and Forward Collision Warning, on

Castro, Gabriel M., Silva, Rita C., Miosso, Cristiano J., Oliveira, Alessandro B. S.

Loss Function Curve to Quantify Customer (Dis)Satisfaction for Rear Opening Width Lower for Notch Back Vehicle

2024-36-0051

12/20/2024

During the early phase of vehicle development, one of the key design attributes to consider is the trunk. Trunk is the pillar that is responsible for user’s accommodate their baggage and make into customer needs in engineer metrics. Therefore, it is one of the key requirements to be considered during the vehicle design. Certain internal vehicle trunk characteristics such as the trunk height and length are engineer metrics that influence the occupants’ perception for trunk. One specific characteristic influencing satisfaction is the rear opening width lower for notch back segment, which is the subject of this paper. The objective of this project is to analyze the relationship between the rear opening width lower with the occupant’s satisfaction under real world driving conditions, based on research, statistical data analysis and dynamic clinics

Santos, Alex Cardoso, Silva, Gustavo, Genaro, Piero, Terra, Rafael, Pádua, Antônio, Benevente, Rodrigo, Lourenço, Sergio

Impact of Replacing a 6-Speed Gearbox with a 5-Speed Gearbox on Vehicle Performance

2024-36-0125

12/20/2024

This study investigates the effects of replacing a 6-speed gearbox with a 5-speed gearbox in a sports vehicle, while keeping all other parameters constant. Through computational simulations, data is collected for comparative performance analysis. The study aims to understand the potential implications of this change on acceleration, fuel efficiency, engine response, as well as aspects such as driver comfort. The results may provide valuable insights for the automotive industry, guiding future transmission design and engineering decisions

Marinho, Gabriel Jannuzzi, de Campos, Josué Queiroz, Lopes, Elias Dias Rossi, Rodrigues, Gustavo Simão

Trajectory Planning and Tracking Control for Single Lane Changing with Different Driving Styles of Intelligent Vehicles Based on Seventh-Degree Polynomial

12-08-03-0028

12/14/2024

Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that

Lai, Fei, Huang, Chaoqun

Human-like Behavior Strategy for Autonomous Vehicles Considering Driving Styles

2024-01-7046

12/13/2024

Autonomous driving technology plays a crucial role in enhancing driving safety and efficiency, with the decision-making module being at its core. To achieve more human-like decision-making and accommodate drivers with diverse styles, we propose a method based on deep reinforcement learning. A driving simulator is utilized to collect driver data, which is then classified into three driving styles—aggressive, moderate, and conservative—using the K-means algorithm. A driving style recognition model is developed using the labeled data. We then design distinct reward functions for the Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) algorithms based on the driving data of the three styles. Through comparative analysis, the SAC algorithm is selected for its superior performance in balancing comfort and driving efficiency. The decision-making models for different styles are trained and evaluated in the SUMO simulation environment. The results indicate that

Shen, Chuanliang, Zhang, Longxu, Shi, Bowen, Ma, Xiaoyuan, Li, Yi, Hu, Hongyu

Interaction-Aware Deep Reinforcement Learning Approach Based on Hybrid Parameterized Action Space for Autonomous Driving

2024-01-7035

12/13/2024

Learning-based motion planning methods such as reinforcement learning (RL) have shown great potential of improving the performance of autonomous driving. However, comprehensively ensuring safety and efficiency remain a challenge for motion planning technology. Most current RL methods output discrete behavioral action or continuous control action, which lack an intuitive representation of the future motion and then face the problems with unstable or reckless driving behavior. To address these issues, this work proposes an interaction-aware reinforcement learning approach based on hybrid parameterized action space for autonomous driving in lane change scenario. The proposed method can output high-level feasible trajectory and low-level actuator control command to control the vehicle’s motion together. Meanwhile, the reward functions for the local traffic environment are designed to evaluate the effect of the interaction between ego vehicle and surrounding vehicles. The contributions of

Li, Zhuoren, Jin, Guizhe, Yu, Ran, Leng, Bo, Xiong, Lu

A Driving Behavior Decision Method Based on Gradient Boosting Tree Algorithm

2024-01-7030

12/13/2024

In order to reduce the incidence of traffic accidents and improve passengers’ driving experience, intelligent driving technology has attracted more and more attention. The core content of intelligent driving technology includes environment perception, behavior decision-making and control follow-up. Simulating driver’s behavior decision-making based on multi-source heterogeneous environment information is the key to liberate drivers and become the focus and difficulty of intelligent driving technology. Aiming at this key problem, this paper presents a design method of driving behavior decision maker based on machine learning after fuzzy classification of historical data. Firstly, 1000 sets of driving environment-decision results database are generated randomly according to driving rules and driving state. A fuzzy classification rule is established to classify driving environment information such as speed and relative distance. Then, a driving behavior decision maker is designed based on

Li, Hongluo, Xia, Hongyang, Huang, Yongxian, Xu, You, Xu, Wei

Driving Behavior Modeling Based on Inverse Reinforcement Learning

2024-01-7029

12/13/2024

With the advancement of intelligent driving technology, today’s smart vehicles must not only make accurate and safe driving decisions but also exhibit high human-likeness to ensure better acceptance from people. Developing vehicle behavior models with increased human-likeness has become a significant industry focus. However, existing vehicle behavior models often struggle to balance human-likeness and interpretability. While some researchers use inverse reinforcement learning (IRL) to model vehicle behavior, ensuring both human-likeness and a degree of interpretability, challenges such as reward function design difficulties and low human-likeness in background vehicle modeling persist. This study addresses these issues by focusing on highway scenarios without on-ramps, specifically following and lane-changing behaviors, using the CitySim dataset. IRL is employed to create a vehicle behavior model with improved human-likeness, utilizing a linear reward function to capture driver

Xu, Xiaobin, Han, Wei, Leng, Bo, Xiong, Lu

Design and Implementation of a Detailed Digital Fuel Indicator System for Accurate Fuel Monitoring

2024-01-5200

12/10/2024

This research introduces a Detailed Digital Fuel Indicator (DDFI) system to enhance fuel monitoring accuracy in automobiles using advanced infrared (IR) sensor technology for precise fuel level detection. The innovative system includes a secondary tank, meticulously calibrated to the volumetric ratio of the primary tank, to ensure consistent and accurate readings. The DDFI system provides real-time data on fuel levels with an impressive accuracy of ±5%, a notable improvement over the traditional methods. Key components of the system include an IR sensor, a programmable integrated circuit (IC), and a secondary tank fabricated from galvanized iron (GI) sheet metal, ensuring durability and reliability in various environmental conditions. The system is designed to be user-friendly, offering an intuitive interface for drivers to monitor fuel levels effortlessly. Additionally, the DDFI system integrates seamlessly with existing vehicle systems, allowing for easy installation and minimal

Mallieswaran, K., Nithya, R., Rajendran, Shuruti, Arulaalan, M.

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