A New Approach To Estimate The Collision Probability For Automotive Applications

We revisit the computation of probability of collision in the context of automotive collision avoidance (the estimation of a potential collision is also referred to as conflict detection in other contexts). After reviewing existing approaches to the definition and computation of a collision probability we argue that the question "What is the probability of collision within the next three seconds?" can be answered on the basis of a collision probability rate. Using results on level crossings for vector stochastic processes we derive a general expression for the upper bound of the distribution of the collision probability rate. This expression is valid for arbitrary prediction models including process noise. We demonstrate in several examples that distributions obtained by large-scale Monte-Carlo simulations obey this bound and in many cases approximately saturate the bound. We derive an approximation for the distribution of the collision probability rate that can be computed on an embedded platform. In order to efficiently sample this probability rate distribution for determination of its characteristic shape an adaptive method to obtain the sampling points is proposed. An upper bound of the probability of collision is then obtained by one-dimensional numerical integration over the time period of interest. A straightforward application of this method applies to the collision of an extended object with a second point-like object. Using an abstraction of the second object by salient points of its boundary we propose an application of this method to two extended objects with arbitrary orientation. Finally, the distribution of the collision probability rate is identified as the distribution of the time-to-collision.Comment: Revised and restructured version, discussion of extended vehicles expanded, section on TTC expanded, references added, other minor changes, 17 pages, 18 figure

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance

Towards computational models for road-user interaction: Drivers overtaking pedestrians and cyclists

Introduction: Crashes resulting from a failed interaction between drivers and vulnerable road users, such as pedestrians or cyclists, can lead to severe injuries or fatalities, especially after failed overtaking maneuvers on rural roads where designated refuge areas are often absent, and impact speeds high. This thesis contains two studies that shed light on driver interaction with either 1) a pedestrian or 2) a cyclist, and oncoming traffic while overtaking. Methods: The first study modeled driver behavior in pedestrian-overtaking maneuvers from naturalistic and field test data, quantifying the effect of the pedestrian’s walking direction and position, as well as the presence of oncoming traffic, on the lateral passing distance and overtaking speed. The second study modeled cyclist-overtaking maneuvers with data from a test-track experiment to quantify how the factors time gap to the oncoming traffic and cyclist lane position affect safety metrics during the maneuver and the overtaking strategy (i.e., flying or accelerative, depending on whether the driver overtook before or after the oncoming traffic had passed, respectively). Results: The results showed that, while overtaking, drivers reduced their safety margins to a pedestrian when the pedestrian was walking against the traffic direction, closer to the lane and when oncoming traffic was present. Results for cyclist overtaking were similar, showing that drivers left smaller safety margins when the cyclist rode closer to the center of the lane or when the time gap to the oncoming traffic was shorter. Under these critical conditions, drivers were more likely to opt for an accelerative maneuver than a flying one. The oncoming traffic had the most influence on drivers’ behavior among all modeling factors, in both pedestrian- and cyclist-overtaking maneuvers. Conclusion: Drivers compromised the risk of a head-on collision with the oncoming traffic by increasing the risk of rear-ending or side-swiping the pedestrian or cyclist. This thesis has implications for infrastructure design, policymaking, car assessment programs, and specifically how vehicular active safety systems may benefit from the developed models to allow more timely and yet acceptable activations

A Review of Near-Collision Driver Behavior Models

Objective: This article provides a review of recent models of driver behavior in on-road collision situations. Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments.<p> Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios.<p> Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms.<p> Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended.<p> Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work