Development and testing of braking and acceleration features for vehicle advanced driver assistance system

<span lang="EN-US">Traffic congestion is a constant problem for cities worldwide. The human driving inefficiency and poor urban planning and development contribute to traffic buildup and travel discomfort. An example of human inefficiency is the phantom traffic jam, which is caused by unnecessary braking, causing traffic to slow down, and eventually coming to a stop. In this study, a brake and acceleration feature (BAF) for the advanced driver assistance system (ADAS) is proposed to mitigate the effects of the phantom traffic phenomenon. In its initial stage, the BAF provides a heads-up display that gives information on how much braking and acceleration input is needed to maintain smooth driving conditions, i.e., without sudden acceleration or deceleration, while observing a safe distance from the vehicle in front. BAF employs a fuzzy logic controller that takes distance information from a light detection and ranging (LIDAR) sensor and the vehicle’s instantaneous speed from the engine control unit (ECU). It then calculates the corresponding percentage value of needed acceleration and braking in order to maintain travel objectives of smooth and safe-distance travel. Empirical results show that the system suggests acceleration and braking values slightly higher than the driver’s actual inputs and can achieve 90% accuracy overall.</span>

The Effect of Social Cohesion on Interest, Usefulness, and Ease of Use of a Driving Assistance System in Older Adults

This study examined the relationship between social cohesion and the perceived interest in, the usefulness of, and the ease of use of an instructor-based driver assistance system in a sample of older adults. With the aging of the population, the use of technologies to support the driving skills of the elderly is expected, and it is necessary to clarify the conditions under which the elderly will be interested in these advanced technologies. Traditionally, social cohesion has been focused on as a function of instrumental and practical support in the lives of the elderly. Since social cohesion reflects the intention to help each other, it could be an opportunity to provide information on advanced driving skill techniques to older people who are becoming more difficult to drive. As an initial exploration, this study examined whether social cohesion was associated with the interest in, the usefulness of, and the ease of use of an instructor-based driver assistance system in 150 elderly people. The results showed that a greater social cohesion was significantly associated with these evaluations, and that a comprehension of the system also contributed. The possession of a license was significantly associated with interest in the program. These findings are an essential step toward the understanding of the roles of social cohesion and positive perception of advanced technology in older adults.

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

Co-DAS: Development and Evaluation of Co-Driver Assistance System Concepts to Reduce Passenger Discomfort

The front seat passenger is often neglected when developing support systems for cars. There exist few examples of systems that provide information or interaction possibilities specifically to those passengers. Previous research indicated that the passive role of the passenger can frequently lead to a feeling of discomfort, potentially caused by missing information and missing control with respect to the driving situation. This paper proposes a variety of prototypical passenger assistance systems that target different aspects of the cognitive processes which could cause the feeling of discomfort. In a simulator study with N = 40 participants, these systems were investigated with respect to their influence on measures of discomfort. Participants experienced different car following and braking scenarios on the highway with different time headways, with and without one of the passenger assistance systems. Based on the subjective measures, three systems were identified as particularly useful in reducing discomfort. For the best of these proposals, more than 63 % of the passengers confirmed the usefulness of the approach and reported an interest in using it in their vehicle. This demonstrates significant opportunities to improve the everyday driving experience beyond classical assistant systems by explicitly taking into account the needs of the passengers.

Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System

Autonomous vehicles and advanced driver assistance systems are predicted to provide higher safety and reduce fuel and energy consumption and road traffic emissions. Lane detection and tracking are the advanced key features of the advanced driver assistance system. Lane detection is the process of detecting white lines on the roads. Lane tracking is the process of assisting the vehicle to remain in the desired path, and it controls the motion model by using previously detected lane markers. There are limited studies in the literature that provide state-of-art findings in this area. This study reviews previous studies on lane detection and tracking algorithms by performing a comparative qualitative analysis of algorithms to identify gaps in knowledge. It also summarizes some of the key data sets used for testing algorithms and metrics used to evaluate the algorithms. It is found that complex road geometries such as clothoid roads are less investigated, with many studies focused on straight roads. The complexity of lane detection and tracking is compounded by the challenging weather conditions, vision (camera) quality, unclear line-markings and unpaved roads. Further, occlusion due to overtaking vehicles, high-speed and high illumination effects also pose a challenge. The majority of the studies have used custom based data sets for model testing. As this field continues to grow, especially with the development of fully autonomous vehicles in the near future, it is expected that in future, more reliable and robust lane detection and tracking algorithms will be developed and tested with real-time data sets.