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We wish to decrease traffic accidents and want to observe drivers' behaviors to find ways to drive safely. Observations at the real environment include many risks to have needless but critical accidents. Using a car driving simulator, we can observe drivers' behaviors in dangerous situations safely. We constructed an immersive car driving simulator...
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... safe driving, training driving skill, car driving simulator, eye tracking application, CAVE, OpenCABIN library. Observing our driving behavior we can objectively know latent driving problems that we do not know before. For example, how slow my brake pedal depression timing was or whether I failed to pay attention to the objects in right timing. If we fix problems, we will be able to drive more safely. To know our problems in our driving, we can drive at dangerous situations many times. But if we drive at many dangerous situation actually, we will have car accidents actually and suffer heavy losses. There are many car driving simulators that can simulate many driving situations safely. Even if we have accidents in those simulators, we do not suffer any losses and we can get experiences of dangerous situations. Many simulators have a small display. We can test some situations in these simulators, but we cannot drive precisely because we cannot recognize whether we are going to hit the wall or not. We cannot recognize relationship between side walls and our car from a normal 2D display. Immersive displays can show objects ’ size to the viewer, so the driver can guess distances from the car to the walls in an immersive car driving simulator. We constructed an immersive car driving simulator that was consisted of K-Cave and a car cockpit system with a precise force-feedback function. In this paper, we describe it and experiments. We constructed an immersive car driving simulator (Figure 1) that was consisted of K-Cave, a precise force-feedback car cockpit simulator and an eye tracking system. K-Cave consisted of 4 screens, 8 projectors, 5 PCs and a magnetic position sensor system: Ascension Technology Corp. ’ s Flock of Bird. The car cockpit simulator consisted of a steering wheel that produced precise force-feedback, a brake pedal and a throttle pedal. The steering wheel and the pedals were parts attached in a real car. The details of the hardware and fundamental software used in our immersive car driving simulator are described in [6]. Figure 2 shows a vehicle model to calculate the vehicle motion. The vehicle model is a single track model which consists of two wheels of the front-rear. This model, developed by Segel in 1956 [4], is currently used to describe vehicle dynamic behavior. Nomenclatures using to derive the vehicle model are presented in the Appendix. The parameters of the vehicle model are corresponding of a real vehicle. Equations of motion of lateral and yaw directions of the single track model are given ...
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Citations
... The motion in the lateral and yaw directions of the vehicle's center of gravity (C.G.) is given by the following equation [22,24,25]. ...
... The figure-eight curve, also known as the Lemniscate of Gerono, possesses a curvature that is exceptionally smooth and richly dispersed. This curve can be parameterized via Equation set (24). ...
The potential of autonomous driving technology to revolutionize the transportation industry has attracted significant attention. Path following, a fundamental task in autonomous driving, involves accurately and safely guiding a vehicle along a specified path. Conventional path-following methods often rely on rule-based or parameter-tuning aspects, which may not be adaptable to complex and dynamic scenarios. Reinforcement learning (RL) has emerged as a promising approach that can learn effective control policies from experience without prior knowledge of system dynamics. This paper investigates the effectiveness of the Deep Deterministic Policy Gradient (DDPG) algorithm for steering control in ground vehicle path following. The algorithm quickly converges and the trained agent achieves stable and fast path following, outperforming three baseline methods. Additionally, the agent achieves smooth control without excessive actions. These results validate the proposed approach’s effectiveness, which could contribute to the development of autonomous driving technology.
While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum.
In Japan, the ratio and the number of elder driver have increased following with the number of traffic accidents which has become a social problem. The problem can easily be solved only if the elders does not need to drive by themselves, although, elders living in the country side or suburban areas still needs to drive for daily activities. Thus, supporting the mobility for the elders and preventing traffic accidents at the same time is very important. Self-driving cars are considered to become one of the solutions to solve this issue, though it is still one of the state-of-art technology still under challenging research. Therefore, this paper proposes a practical approach to test functions and interfaces of the self-driving car for elder drivers. In particular, this paper features use of an immersive virtual reality environment for a human-in-the-loop simulation test.
Navigation systems are nowadays widely used for cars though it is yet to be able to say popularized for motorcycles. While motorcycle navigation systems are not popularized yet, previous research indicates motorcyclist's high demand against a useful navigation system. The absence of useful motorcycle navigation system is an issue of current products not capable of providing navigation information efficiently. To work with the issue, information presentation design is necessary to consider the motorcyclist's characterful viewpoint movement of looking at the road surface carefully in a vertical movement. As a solution to this issue, we propose the use of head-up display for information presentation. Previous studies have revealed the amount and positions suitable to present information for motorcyclist while riding, although the timing of information presentation is yet to be discussed. Thus, in this paper, the information presentation timing to provide navigation information has been evaluated. Experiment using an immersive CAVE motorcycle simulator was conducted with the configuration of five timings between 25 m to 85 m prior to the intersection under conditions of urban street with 30 km/h speed limit. Durations of motorcyclist's viewpoint movement and five scale subjective ratings were used for evaluation. The experimental results from 10 subjects showed a statistically significant difference in subjective ratings. In conclusion, suitable information presentation timing of riding in urban streets with 30 km/h speed limit is around 40 m to 55 m prior to the target intersection.
