Behavior Of Other Traffic Participants Research Articles

Information fusion for online estimation of the behavior of traffic participants using belief function theory

Motion planning algorithms for automated vehicles need to assess the intended behavior of other Traffic Participants (TPs), in order to predict the likely future trajectory of TPs and plan the motion consequently. Information resulting from several sources, like sensors, must be gathered and combined into a reliable estimate of the intended behavior of TPs. Such estimates must be sufficiently steady and quantify the inherent uncertainty around the assessment. We present a novel information fusion algorithm to combine information from different sources into a coherent and reliable estimate. To explicitly account for the uncertainty of estimates, we leverage the Belief Function Theory and evaluate and handle possible disagreements between estimates individually provided by the sources. The algorithm is flexible and can also handle sources that do not discern between some of the considered behaviors and are only capable of assessing the probability of unions or clusters of different behaviors. We discuss the strengths of the approach through simulations in SUMO, comparing it to the Interactive Multiple Model algorithm.

Reducing uncertainty by anticipation in traffic – The effect of situational characteristics and criticality on behavioral, subjective, and psychophysiological parameters

Uncertainty in traffic may have fatal consequences when operating a vehicle. Enabling drivers to anticipate the behavior of other traffic participants can help reduce uncertainty and thus increase traffic safety. A multi-method approach using behavioral, subjective, and psychophysiological measures was applied to investigate the relation of uncertainty and anticipation. Participants (N = 30) observed simulated, video-based urban traffic scenarios while skin conductance responses were recorded. They had to indicate, by pressing a button, (1) the moment they first thought another vehicle might merge into their lane (low certainty anticipation) and (2) the moment they were sure another vehicle would merge (high certainty anticipation). Situational characteristics served as anticipatory cues that helped predict the other vehicle’s action. On the one hand, in this study, target cues (which are clearly related to the target’s activity) served to indicate an imminent lane change. On the other hand, context cues represented visible precursors in the traffic environment, e.g. a traffic sign pointing to upcoming road work. In addition, causal cues were used to determine a reason for the other vehicle to change lanes (in this instance, a construction site blocking the lane). These situational characteristics, as well as the situational criticality, were manipulated to gain insights into factors influencing the process of anticipation. Results offered an effect of target cue moderated by criticality: especially in more critical situations, the anticipation rate and subjective certainty increased, and physiological activation was reduced with target cues. Overall, the anticipatory performance was found to be a predictor of subjective certainty (through its impact on skin conductance responses). The findings are discussed in the context of the methodological approach for applications in traffic.

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Provably safe motion planning for automated road vehicles must ensure that planned motions do not result in a collision with other traffic participants. This is a major challenge in autonomous driving, since the future behavior of other traffic participants is not known and since traffic participants are often hidden due to occlusions. In this work, we propose a formal set-based prediction that contains all acceptable future behaviors of both detected and potentially hidden traffic participants. Based on formalized traffic rules and nondeterministic motion models, we perform reachability analysis to predict the set of possible occupancies and velocities of vehicles, pedestrians, and cyclists. Real-world experiments with a test vehicle in various traffic situations demonstrate the applicability and real-time capability of our over-approximative prediction for both online verification and fail-safe trajectory planning. Even in congested, complex traffic scenarios, our forecasting approach enables self-driving vehicles to never cause accidents.

An Investigation of Vehicle Behavior Prediction Using a Vector Power Representation to Encode Spatial Positions of Multiple Objects and Neural Networks.

Predicting future behavior and positions of other traffic participants from observations is a key problem that needs to be solved by human drivers and automated vehicles alike to safely navigate their environment and to reach their desired goal. In this paper, we expand on previous work on an automotive environment model based on vector symbolic architectures (VSAs). We investigate a vector-representation to encapsulate spatial information of multiple objects based on a convolutive power encoding. Assuming that future positions of vehicles are influenced not only by their own past positions and dynamics (e.g., velocity and acceleration) but also by the behavior of the other traffic participants in the vehicle's surroundings, our motivation is 3-fold: we hypothesize that our structured vector-representation will be able to capture these relations and mutual influence between multiple traffic participants. Furthermore, the dimension of the encoding vectors remains fixed while being independent of the number of other vehicles encoded in addition to the target vehicle. Finally, a VSA-based encoding allows us to combine symbol-like processing with the advantages of neural network learning. In this work, we use our vector representation as input for a long short-term memory (LSTM) network for sequence to sequence prediction of vehicle positions. In an extensive evaluation, we compare this approach to other LSTM-based benchmark systems using alternative data encoding schemes, simple feed-forward neural networks as well as a simple linear prediction model for reference. We analyze advantages and drawbacks of the presented methods and identify specific driving situations where our approach performs best. We use characteristics specifying such situations as a foundation for an online-learning mixture-of-experts prototype, which chooses at run time between several available predictors depending on the current driving situation to achieve the best possible forecast.

General Behavior Prediction by a Combination of Scenario-Specific Models

Before taking a decision, a driver anticipates the future behavior of other traffic participants. However, if a driver is inattentive or overloaded, he may fail to consider relevant information. This can lead to bad decisions and potentially result in an accident. A computational system that is designed to anticipate other traffic participants' behaviors could assist the driver in his decision making by sending him an early warning when a risk of collision is predicted. Existing research in this area usually focuses on only one of two aspects, i.e., quality or scope. Quality refers to the ability to warn a driver early before a dangerous situation happens. Scope is the diversity of scenarios in which the approach can work. In general, we see methods targeting a broad scope but showing low quality, with others having a narrow scope but high quality. Our goal is to create a system with high quality and high scope. To achieve this, we propose an architecture that combines classifiers to predict behaviors for many scenarios. In this paper, we will first introduce the generic concept of such a system applicable to highway and inner-city scenarios. We will show that a combination of general and specific classifiers is a solution to improve quality and scope based on a concrete implementation for lane-change prediction in highway scenarios.