Abstract

Due to driver assistance systems and the trend of industry towards automated driving, the hands and feet of the driver in a vehicle require less intervention, becoming even idle. Recent gesture recognition focuses on hand interaction. This paper provides feet gesture interaction. Many gesture recognition systems rely on computing intensive video systems, causing privacy concerns. Furthermore, these systems require a line of sight and therefore a visible interior design integration. Our system proves that invisibly integrated capacitive proximity sensors can do this as well. They do not cause privacy issues and can be integrated under non-conductive materials. Therefore, there is no impact on the design of the visible interior. Our proposed solution distinguishes between four feet gestures. There is no limitation to feet movement. Further, we prove the functionality of the system in an evaluation with six participants, using a prototypical mock-up of a vehicle legroom. %This project contributes to the basis of driver feet gesture recognition pointing to further applications and more comprehensive investigations.

Abstract

To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To solve this problem, this paper presents an automatic regenerative control system based on a deceleration model that reflects the driver&rsquo s characteristics. The deceleration model is designed as a parametric model that mimics the driver&rsquo s behavior. In addition, it consists of parameters that represent the driver&rsquo s characteristics. These parameters are updated online by a learning algorithm. The validation results of the vehicle testing show that the vehicle maintained a safe distance from the leading car while simulating a driver&rsquo s behavior. Of all the deceleration that occurred during the testing, 92% was conducted by the automatic regeneration system. In addition, the results of the online learning algorithm are different based on the driver&rsquo s deceleration pattern. The presented automatic regenerative control system can be safely used in diverse car-following situations. Moreover, the system&rsquo s acceptability is improved by updating the driver characteristics. In the future, the algorithm will be extended for use in more diverse deceleration situations by using intelligent transportation system information. Document type: Article

Abstract

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system. Document type: Article