Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.Comment: Submitted to 2014 International Workshop on the Algorithmic Foundations of Robotic

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

For safe navigation in dynamic environments, an autonomous vehicle must be able to identify and predict the future behaviors of other mobile agents. A promising data-driven approach is to learn motion patterns from previous observations using Gaussian process (GP) regression, which are then used for online prediction. GP mixture models have been subsequently proposed for finding the number of motion patterns using GP likelihood as a similarity metric. However, this paper shows that using GP likelihood as a similarity metric can lead to non-intuitive clustering configurations - such as grouping trajectories with a small planar shift with respect to each other into different clusters - and thus produce poor prediction results. In this paper we develop a novel modeling framework, Dirichlet process active region (DPAR), that addresses the deficiencies of the previous GP-based approaches. In particular, with a discretized representation of the environment, we can explicitly account for planar shifts via a max pooling step, and reduce the computational complexity of the statistical inference procedure compared with the GP-based approaches. The proposed algorithm was applied on two real pedestrian trajectory datasets collected using a 3D Velodyne Lidar, and showed 15% improvement in prediction accuracy and 4.2 times reduction in computational time compared with a GP-based algorithm.Ford Motor Compan

Decision-theoretic MPC: Motion Planning with Weighted Maneuver Preferences Under Uncertainty

Continuous optimization based motion planners require deciding on a maneuver homotopy before optimizing the trajectory. Under uncertainty, maneuver intentions of other participants can be unclear, and the vehicle might not be able to decide on the most suitable maneuver. This work introduces a method that incorporates multiple maneuver preferences in planning. It optimizes the trajectory by considering weighted maneuver preferences together with uncertainties ranging from perception to prediction while ensuring the feasibility of a chance-constrained fallback option. Evaluations in both driving experiments and simulation studies show enhanced interaction capabilities and comfort levels compared to conventional planners, which consider only a single maneuver

Multi-Policy Decision Making for Reliable Navigation in Dynamic Uncertain Environments

Navigating everyday social environments, in the presence of pedestrians and other dynamic obstacles remains one of the key challenges preventing mobile robots from leaving carefully designed spaces and entering our daily lives. The complex and tightly-coupled interactions between these agents make the environment dynamic and unpredictable, posing a formidable problem for robot motion planning. Trajectory planning methods, supported by models of typical human behavior and personal space, often produce reasonable behavior. However, they do not account for the future closed-loop interactions of other agents with the trajectory being constructed. As a consequence, the trajectories are unable to anticipate cooperative interactions (such as a human yielding), or adverse interactions (such as the robot blocking the way). Ideally, the robot must account for coupled agent-agent interactions while reasoning about possible future outcomes, and then take actions to advance towards its navigational goal without inconveniencing nearby pedestrians. Multi-Policy Decision Making (MPDM) is a novel framework for autonomous navigation in dynamic, uncertain environments where the robot's trajectory is not explicitly planned, but instead, the robot dynamically switches between a set of candidate closed-loop policies, allowing it to adapt to different situations encountered in such environments. The candidate policies are evaluated based on short-term (five-second) forward simulations of samples drawn from the estimated distribution of the agents' current states. These forward simulations and thereby the cost function, capture agent-agent interactions as well as agent-robot interactions which depend on the ego-policy being evaluated. In this thesis, we propose MPDM as a new method for navigation amongst pedestrians by dynamically switching from amongst a library of closed-loop policies. Due to real-time constraints, the robot's emergent behavior is directly affected by the quality of policy evaluation. Approximating how good a policy is based on only a few forward roll-outs is difficult, especially with the large space of possible pedestrian configurations and the sensitivity of the forward simulation to the sampled configurations. Traditional methods based on Monte-Carlo sampling often missed likely, high-cost outcomes, resulting in an over-optimistic evaluation of a policy and unreliable emergent behavior. By re-formulating policy evaluation as an optimization problem and enabling the quick discovery of potentially dangerous outcomes, we make MPDM more reliable and risk-aware. Even with the increased reliability, a major limitation is that MPDM requires the system designer to provide a set of carefully hand-crafted policies as it can evaluate only a few policies reliably in real-time. We radically enhance the expressivity of MPDM by allowing policies to have continuous-valued parameters, while simultaneously satisfying real-time constraints by quickly discovering promising policy parameters through a novel iterative gradient-based algorithm. Overall, we reformulate the traditional motion planning problem and paint it in a very different light --- as a bilevel optimization problem where the robot repeatedly discovers likely high-cost outcomes and adapts its policy parameters avoid these outcomes. We demonstrate significant performance benefits through extensive experiments in simulation as well as on a physical robot platform operating in a semi-crowded environment.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150017/1/dhanvinm_1.pd