Lane-change detection using a computational driver model

OBJECTIVE: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. METHOD: Using a "model tracing" methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. RESULTS: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. CONCLUSION: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. APPLICATION: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.     

The species fitness method for the evolution of cooperative behavior in a group task

This paper considers the evolution of cooperative behaviors as the interaction among agents using a genetic algorithm to improve the performance of the task in a group (group performance). Previous research often usedthe group fitness method, which evaluates group performance for the evolution of multiple groups in parallel. However, this entails large simulation costs and the evolution speed is slow.The individual fitness method that evaluates theindividual performance of the task entails a smaller simulation cost. However, it can not improve the group performance since each agent behaves selfishly. To optimize the group performance, it is important to include bothcompetition andsharing. Therefore, this paper presentsthe species fitness method, which shares the individual performances of agents belonging to the same species in a group that all have the same chromosomes. We show comparative experiments on these three methods on the evolutionary simulation of a foraging task in a group. To test the interaction among the agents, four kinds of species are evolved which show their communication ability by demonstrating whether the agent can send or receive the signal for food. Experimental results show that evaluating the species variance fitness leads the agents into reciprocative actions. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Construction of living cellular automata using the Physarum plasmodium

The plasmodium of Physarum polycephalum is a unicellular and multinuclear giant amoeba that has an amorphous cell body. To clearly observe how the plasmodium makes decisions in its motile and exploratory behaviours, we developed a new experimental system to pseudo-discretize the motility of the organism. In our experimental space that has agar surfaces arranged in a two-dimensional lattice, the continuous and omnidirectional movement of the plasmodium was limited to the stepwise one, and the direction of the locomotion was also limited to four neighbours. In such an experimental system, a cellular automata-like system was constructed using the living cell. We further analysed the exploratory behaviours of the plasmodium by duplicating the experimental results in the simulation models of cellular automata. As a result, it was revealed that the behaviours of the plasmodium are not reproduced by only local state transition rules; and for the reproduction, a kind of historical rule setting is needed.     

High‐efficiency light‐collection optics for lamp‐based projection TV

Abstract— A new light‐collection optics has been developed that enhances the luminance of projection TV which use lamps as the light source. The conventional optical system consists of an elliptical reflector and a flat‐surface front glass, but these systems cannot sufficiently collect the beams coming from the light source, and they cause loss in the coupling with the light pipe. To solve this problem, we devised a new optical system through a structure of an aspherical reflector and an aspherical front glass. This new optical system concentrates the beams coming from the light source to a smaller point which improves the coupling efficiency. Thus, we have successfully increased the luminance of the projection TV by approximately 10%. This paper reports the design principles of the new optical system and the results of a prototype experiment.     

A biped static balance control and torque pattern learning under unknown periodic external forces

This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called ‘ankle strategy’ model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces.     

Do people with social anxiety feel anxious about interacting with a robot?

AI & SOCIETY - To investigate whether people with social anxiety have less actual and “anticipatory” anxiety when interacting with a robot compared to interacting with a person, we...     

Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial,spectral and temporal modeling of sounds

Research on noise robust speech recognition has mainly focused on dealing with relatively stationary noise that may differ from the noise conditions in most living environments. In this paper, we introduce a recognition system that can recognize speech in the presence of multiple rapidly time-varying noise sources as found in a typical family living room. To deal with such severe noise conditions, our recognition system exploits all available information about speech and noise; that is spatial (directional), spectral and temporal information. This is realized with a model-based speech enhancement pre-processor, which consists of two complementary elements, a multi-channel speech–noise separation method that exploits spatial and spectral information, followed by a single channel enhancement algorithm that uses the long-term temporal characteristics of speech obtained from clean speech examples. Moreover, to compensate for any mismatch that may remain between the enhanced speech and the acoustic model, our system employs an adaptation technique that combines conventional maximum likelihood linear regression with the dynamic adaptive compensation of the variance of the Gaussians of the acoustic model. Our proposed system approaches human performance levels by greatly improving the audible quality of speech and substantially improving the keyword recognition accuracy.