In interactions between automated vehicles (AVs) and crossing pedestrians, modeling implicit vehicle communication is crucial. In this work, we present a combined prediction and planning approach that allows to consider the influence of the planned vehicle behavior on a pedestrian and predict a pedestrian's reaction. We plan the behavior by solving two consecutive optimal control problems (OCPs) analytically, using variational calculus. We perform a validation step that assesses whether the planned vehicle behavior is adequate to trigger a certain pedestrian reaction, which accounts for the closed-loop characteristics of prediction and planning influencing each other. In this step, we model the influence of the planned vehicle behavior on the pedestrian using a probabilistic behavior acceptance model that returns an estimate for the crossing probability. The probabilistic modeling of the pedestrian reaction facilitates considering the pedestrian's costs, thereby improving cooperative behavior planning. We demonstrate the performance of the proposed approach in simulated vehicle-pedestrian interactions with varying initial settings and high-light the decision making capabilities of the planning approach.

Interaction modeling plays a huge role in understanding human behavior in traffic. This is especially relevant when it comes to interactions between vehicles and vulnerable road users such as pedestrians. Thus, pedestrian intention prediction is an ongoing field of research in order to understand the pedestrians' decision making. Most state-of-the-art prediction frameworks are trained on large-scale datasets and evaluated with respect to acknowledged benchmarks. These datasets lack the ability to account for the reciprocal nature of interactions between pedestrians and vehicles and the effects of the two agents influencing each other. In this work, we demonstrate first steps towards assessing pedestrian prediction algorithms within realistic scenarios including the interaction effects arising from its interplay with a planning component. For this, we validate an existing prediction framework trained on benchmark datasets with situations from a virtual reality (VR) pedestrian-vehicle co-simulator that allows us to include the effect of vehicle planning on pedestrian behavior. We evaluate the performance of the prediction framework comparing data from pre-recorded real-world datasets with data from our co-simulation study and conduct an ablation analysis to identify the most important features for pedestrian intention prediction. The results highlight the significance of pedestrian action and proximity to the road.

In Automated Driving (AD), traffic scenarios where two agents must resolve an ordering without knowing each other's intention are critical for expanding the operational design domain of automated vehicles to urban environments. These scenarios require negotiation to determine who passes first through an interaction zone. We present a novel agreement measure and negotiation approach to resolve these ordering problems across a wide range of common scenarios. Our method emphasizes detecting and deciding when to switch between potential negotiation outcomes. Our approach extends existing behavior planners to cope with bimodal cooperative interactions, where two potentially desirable outcomes need to be considered. We evaluate our approach by providing both an illustrative scenario and extensive statistical experiments across various geometries, including oncoming narrow passages, crossing and merging scenarios. The results demonstrate that our system considerably improves the behavior in cooperative ordering scenarios compared to the baseline. Furthermore, it is also robust in the sense that it effectively handles dynamic situations where the other agent's intentions changes during the negotiation process.

In Social Robot Navigation, autonomous agents need to resolve many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to select a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning into two separate steps: Conflict avoidance for finding good, macroscopic trajectories, and cooperative collision avoidance (CCA) for resolving the next interaction optimally. We propose the Probabilistic Gap Planner (PGP) as a conflict avoidance planner. PGP modifies an established probabilistic collision risk model to include a general assumption of cooperativity. PGP biases the short-term CCA planner to head towards gaps in the crowd. In extensive simulations with crowds of varying density, we show that using PGP in addition to state-of-the-art CCA planners improves the agents’ performance: On average, agents keep more space to others, create less tension, and cause fewer collisions. This typically comes at the expense of slightly longer paths. PGP runs in real-time on WaPOCHI mobile robot by Honda R&D.

Interactions between humans and automated vehicles (AVs) will become increasingly common in future traffic. Cooperative behavior enables comfortable and efficient resolutions of such interactions through joint actions. Thus, AVs need the ability to communicate their intention and planned behavior to outside road users to facilitate cooperation among the interaction partners. Many recent studies suggest external human-machine-interfaces (eHMIs) as a feasible way to realize the communication between AVs and other road users. These studies often focus on the design of eHMIs and evaluate their usability based on subjective measures from the perspective of the outside road user. There is only little research on the objective benefits of explicit external communication on the resolution of interactions with AVs. The decision making behind the activation of the communication and the consideration of the actual interaction in the communication are still open research topics. In this work, we present a communication framework that facilitates cooperation between an AV and pedestrians by communicating the vehicle’s yielding intention to give right of way. We demonstrate the proposed system’s ability to improve the joint utility as well as the driving comfort by empirically evaluating simulated interactions between AVs and pedestrians. The results indicate that timely communication leads to more efficient and more predictable pedestrian-vehicle interactions.

Driver support systems that include human states in the support process is an active research field. Many recent approaches allow, for example, to sense the driver’s drowsiness or awareness of the driving situation. However, so far, this rich information has not been utilized much for improving the effectiveness of support systems. In this paper, we therefore propose a warning system that uses human states in the form of driver errors and can warn users in some cases of upcoming risks several seconds earlier than the state of the art systems not considering human factors. The system consists of a behavior planner Risk Maps which directly changes its prediction of the surrounding driving situation based on the sensed driver errors. By checking if this driver’s behavior plan is objectively safe, a more robust and foresighted driver warning is achieved. In different simulations of a dynamic lane change and intersection scenarios, we show how the driver’s behavior plan can become unsafe, given the estimate of driver errors, and experimentally validate the advantages of considering human factors.

Many everyday traffic situations require cooperation among traffic participants to establish the order in which they pass a shared part of the road. Behavior planners which do not take this cooperative aspect into account properly struggle to find efficient solutions if the situation is nontrivial. Improper modelling may lead to overly aggressive or conservative behavior. In this paper, we propose an extension to state-of-the-art systems that enables behavior planners to efficiently cope with narrow passage scenarios even without car-to-car communication. The extended system is based on an asymmetry measure which takes the shared traffic space and the cooperation partners into account. This measure is then used to continuously predict which potential outcome is more likely to occur, to infer the assumed strategy of the cooperation partner, and to match the own strategy accordingly. Experiments show that the proposed system significantly reduces the cumulative passing time of the shared traffic space as compared to baseline systems. The resulting solutions are robust against variations in the behavior of both cooperation partners, and explicitly account for oblivious traffic participants which behave uncooperatively.

Estimation of Distribution Algorithms (EDAs) are metaheuristics where learning a model and sampling new solutions replaces the variation operators recombination and mutation used in standard Genetic Algorithms. The choice of these models as well as the corresponding training processes are subject to the bias/variance tradeoff, also known as under- and overfitting: simple models cannot capture complex interactions between problem variables, whereas complex models are susceptible to modeling random noise. This paper suggests using Denoising Autoencoders (DAEs) as generative models within EDAs (DAE-EDA). The resulting DAE-EDA is able to model complex probability distributions. Furthermore, overfitting is less harmful, since DAEs overfit by learning the identity function. This overfitting behavior introduces unbiased random noise into the samples, which is no major problem for the EDA but just leads to higher population diversity. As a result, DAE-EDA runs for more generations before convergence and searches promising parts of the solution space more thoroughly. We study the performance of DAE-EDA on several combinatorial single-objective optimization problems. In comparison to the Bayesian Optimization Algorithm, DAE-EDA requires a similar number of evaluations of the objective function but is much faster and can be parallelized efficiently, making it the preferred choice especially for large and difficult optimization problems.

We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density. A merge-in support system has to estimate the chances that a gap between two consecutive vehicles can be taken successfully. In contrast to previous models based on heuristic gap size rules, we present an approach which optimizes the integral risk of the situation using parametrized velocity ramps. It accounts for the risks from curves and all involved vehicles (front and rear on all paths) with a so-called survival analysis. For comparison, we also introduce a specially designed extension of the Intelligent Driver Model (IDM) for entering intersections. We show in a quantitative statistical evaluation that the survival method provides advantages in terms of lower absolute risk (i.e., no crash happens) and better risk-utility tradeoff (i.e., making better use of appearing gaps). Furthermore, our approach generalizes to more complex situations with additional risk sources.

We present a parallel version of RBM-EDA. RBM-EDA is an Estimation of Distribution Algorithm (EDA) that models dependencies between decision variables using a Restricted Boltzmann Machine (RBM). In contrast to other EDAs, RBM-EDA mainly uses matrix-matrix multiplications for model estimation and sampling. Hence, for implementation, standard libraries for linear algebra can be used. This allows an easy parallelization and leads to a high utilization of parallel architectures. The probabilistic model of the parallel version and the version on a single core are identical. We explore the speedups gained from running RBM-EDA on a Graphics Processing Unit. For problems of bounded difficulty like deceptive traps, parallel RBM-EDA is faster by several orders of magnitude (up to 750 times) in comparison to a single-threaded implementation on a CPU. As the speedup grows linearly with problem size, parallel RBM-EDA may be particularly useful for large problems.