Ocullo

Simulating Pedestrian Movement

Introduction

This paper proposes a novel approach for simulating pedestrian movement behavior based on artificial intelligence technology. Within this approach, a large volume of microscopic pedestrian movement behavior types were collected and encapsulated into an artificial neural network via network training. The trained network was then fed back into a simulation environment to predict the pedestrian movement. Two simulation experiments were conducted to evaluate the performance of the approach. First, a pedestrian-collision-avoidance test was conducted, and the results showed that virtual pedestrians with learned pedestrian behavior can move reasonably to avoid potential collisions with other pedestrians. In addition, a critical parameter, i.e., defined as “reacting distance” and determined to be 2.5 m, represented the boundary of the collision buffer zone. Second, a pedestrian counterflow in a road-crossing situation was simulated, and the results were compared with the real-life scenario. The comparison revealed that the pedestrian distributions, erratic trajectories, and density–speed fundamental diagram in the simulation are reasonably consistent with the real-life scenario. Furthermore, a quantitative indicator, i.e., the relative distance error, was calculated to evaluate the simulation error of pedestrians’ trajectories between the simulation and the real-life scenario, the mean of which was calculated to be 0.322. This revealed that the simulation results were acceptable from an engineering perspective, and they also showed that the approach could reproduce the lane-formation phenomenon. We considered the proposed approach to be capable of simulating human-like microscopic pedestrian flow.
Researchers at the University of Michigan are teaching self-driving cars to recognize and predict pedestrians’ movements with greater precision than current technologies by zeroing in on their gait, body symmetry, and foot placement. Data collected by vehicles through cameras, LiDAR, and GPS let the researchers capture video snippets of humans in motion and then recreate them in 3D computer simulations. With that, they’ve created a “biomechanically inspired recurrent neural network” that catalogs human movements. The network lets AI machines predict poses and future locations for one or several pedestrians up to 50 yards from the vehicle. That’s about the size of a city intersection. “Prior work in this area typically looked at only still images—it wasn’t concerned with how people move in three dimensions,” says Ram Vasudevan, a UM assistant professor of mechanical engineering. “But if vehicles are going to operate and interact in the real world, we need to make sure our predictions of where a pedestrian is going doesn’t coincide with where the vehicle is going next.” Equipping vehicles with the necessary predictive power requires that the network dive into the minutiae of human movement: the pace of a human’s gait (periodicity), the mirror symmetry of limbs, and the way foot placement affects stability during walking. Much of the machine learning used to bring autonomous technology to its current level has dealt with two-dimensional images—still photos. A computer shown several million photos of a stop sign will eventually come to recognize stop signs in the real world and in real time. But using video clips that run for several seconds lets researchers study the first half of the snippet to make predictions, and then verify the accuracy with the second half. “Now, we’re training the system to recognize motion and making predictions of not just one single thing—whether it’s a stop sign or not—but where that pedestrian’s body will be at the next step and the next and the next,” says Matthew Johnson-Roberson, an associate professor in UM’s Department of Naval Architecture and Marine Engineering. To explain the kind of extrapolations the neural network can make, Vasudevan describes a common sight. “If a pedestrian is playing with their phone, you know they’re distracted,” he explains. “Their pose and where they’re looking tells you a lot about their level of attentiveness. It’s also telling you a lot about what they can do next.” Results show that this new approach improves a driverless vehicle’s ability to predict what’s most likely to happen next.
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