Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference

The discomfort of riding shotgun – Why many people don’t like to be co-driver

This work investigates which conditions lead to co-driver discomfort aside from classical motion sickness, what characterizes uncomfortable situations and why these conditions lead to discomfort. The automobile is called a “passenger vehicle” as its main purpose is the transportation of people. However, passengers in the car are rarely considered in research about driving discomfort. The few studies in this area focus on driver discomfort, automated vehicles, and on driver assistant systems. An earlier public survey indicated that discomfort is also a relevant problem for co-drivers. An online questionnaire with N = 119 participants and a detailed follow up interview study with N = 24 participants were conducted. The results of the online questionnaire show that co-driver discomfort is a widespread problem (88 %). The results of the interviews indicate that the driving style is the only reason rated as very influential. Frequently mentioned reasons for discomfort are close following or fast driving. Uncomfortable situations were often perceived as safety critical. Participants also felt exposed to these situations. A model for possible cognitive origins of discomfort in co-drivers is proposed based on the study results. Co-driver discomfort is a common problem, highlighting the relevance of further research on supporting co-drivers. The reported correlations and the extension of theories from the areas of stress and self-regulation can help to explain the origin of this discomfort. The results provide a foundation for future design of interventions like human machine interfaces aiming at reducing co-driver discomfort

Accurate Behavior Prediction on Highways Based on a Systematic Combination of Classifiers

Bonnin S, Weisswange TH, Kummert F, Schmuedderich J. Accurate Behavior Prediction on Highways Based on a Systematic Combination of Classifiers. In: 2013 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV). IEEE Intelligent Vehicles Symposium. New York: Ieee; 2013: 242-249.To drive safely, a good driver observes his surroundings, anticipates the actions of other traffic participants and then decides for a maneuver. But if a driver is inattentive or overloaded he may fail to include some relevant information. This can then lead to wrong decisions and potentially result in an accident. In order to assist a driver in his decision making, Advanced Driver Assistance Systems (ADAS) are becoming more and more popular in commercial cars. The quality of these existing systems compared to an experienced driver is weak, because they rely purely on physical observation and thus react shortly before an accident. For an earlier warning of the driver behavior prediction is used. We classify existing research in this area with respect to two aspects: quality and scope. Quality means the ability to warn a driver early before a dangerous situation. Scope means the diversity of scenes in which the approach can work. In general we see two tendencies, methods targeting for broad scope but having low quality and those targeting for narrow scope but high quality. Our goal is to have a system with high quality and wide scope. To achieve this, we propose a system that combines classifiers to predict behaviors for many scenarios. To show that a combination of general and specific classifiers is a solution to improve quality and scope, this paper will introduce the generic concept of our system followed by a concrete implementation for lane change prediction for highway scenarios

Pedestrian crossing prediction using multiple context-based models

Bonnin S, Weisswange TH, Kummert F, Schmuedderich J. Pedestrian crossing prediction using multiple context-based models. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). Institute of Electrical and Electronics Engineers (IEEE); 2014

General Behavior Prediction by a Combination of Scenario-Specific Models

Bonnin S, Weisswange TH, Kummert F, Schmuedderich J. General Behavior Prediction by a Combination of Scenario-Specific Models. IEEE Transactions on Intelligent Transportation Systems. 2014;15(4):1478-1488

Hybrid Eyes:Design and Evaluation of the Prediction-Level Cooperative Driving with a Real-World Automated Driving System

While automated driving systems (ADS) have progressed fast in recent years, there are still various situations in which an ADS cannot perform as well as a human driver. Being able to anticipate situations, particularly when it comes to predicting the behaviour of surrounding traffic, is one of the key elements for ensuring safety and comfort. As humans are still surpassing state-of-the-art ADS in this task, this led to the development of a new concept, called prediction-level cooperation, in which the human can help the ADS to better anticipate the behaviour of other road users. Following this concept, we implemented an interactive prototype, called Prediction-level Cooperative Automated Driving system (PreCoAD), which allows human drivers to intervene in an existing ADS that has been validated on the public road, via gaze-based input and visual output. In a driving simulator study, 15 participants drove different highway scenarios with plain automation and with automation using the PreCoAD system. The results show that the PreCoAD concept can enhance automated driving performance and provide a positive user experience. Follow-up interviews with participants also revealed the importance of making the system's reasoning process more transparent.</p

Building a probabilistic grid-based road representation from direct and indirect visual cues.

Casapietra E, Weisswange TH, Goerick C, Kummert F, Fritsch J. Building a probabilistic grid-based road representation from direct and indirect visual cues. In: IEEE Intelligent Vehicles Symposium. 2015: 273-279