Probabilistic Threat Assessment and Driver Modeling in Collision Avoidance Systems

This paper presents a probabilistic framework for decision-making in collision avoidance systems, targeting all types of collision scenarios with all types of single road users and objects. Decisions on when and how to assist the driver are made by taking a Bayesian approach to estimate how a collision can be avoided by an autonomous brake intervention, and the probability that the driver will consider the intervention as motivated. The driver model makes it possible to initiate earlier braking when it is estimated that the driver acceptance for interventions is high. The framework and the proposed driver model are evaluated in several scenarios, using authentic tracker data and a differential GPS. It is shown that the driver model can increase the benefit of collision avoidance systems — particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict, e.g. when a playing child enters the roadway

Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program

Probabilistic Decision Making for Collision Avoidance Systems: Postponing Decisions

International audienceFor collision avoidance systems to be accepted by human drivers, it is important to keep the rate of unnecessary interventions very low. This is challenging since the decision to intervene or not is based on incomplete and uncertain information. The contribution of this paper is a decision making strategy for collision avoidance systems which allows the system to occasionally postpone a decision in order to collect more information. The problem is formulated in the framework of statistical decision theory, and the core of the algorithm is to run a preposterior analysis to estimate the benefit of deciding with the additional information. A final decision is made by comparing this benefit with the cost of delaying the intervention. The proposed approach is evaluated in simulation at a two-way stop road intersection for stop sign violation scenarios. The results show that the ability to postpone decisions leads to a significant reduction of false alarms and does not impair the ability of the collision avoidance system to prevent accidents

Cooperative Path-Planning for Multi-Vehicle Systems

In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles’ position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently

Modelling discomfort: How do drivers feel when cyclists cross their path?

Introduction: Even as worldwide interest in bicycling continues to grow, cyclists constitute a large part of road fatalities. A major part of the fatalities occurs when cyclists cross a vehicle path. Active safety systems and automated driving systems may already account for these interactions in their control algorithms. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. If the systems could estimate driver discomfort, their acceptance might be improved.Method: This study investigated the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. The effects of demographic variables (age, gender, driving frequency, and yearly mileage), controlled variables (car speed, bicycle speed, and bicycle-car configuration), and a visual cue (car’s time-to-arrival at the intersection when the bicycle appears; TTAvis) on self-reported discomfort were analysed using cumulative link mixed models (CLMM).Results: Results showed that demographic variables had a significant effect on the discomfort felt by drivers—and could explain the variability observed between drivers. Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTAvis was shown to have a significant effect on discomfort as well; the closer to zero TTAvis was (i.e., the more critical the situation), the more likely the driver red great discomfort. The prediction accuracies of the CLMM with controlled variables and the CLMM with the visual cue were similar, with an average accuracy between 40 and 50%. Surprise trials in the simulator experiment, in which the bicycle appeared unexpectedly, improved the prediction accuracy of the models, more notably the CLMM including TTAvis. Conclusions: The results suggest that the discomfort was mainly driven by the visual cue rather than the deceleration cues. Thus, it is suggested that an algorithm that estimates driver discomfort be included in active safety systems and autonomous driving systems. The CLMM including TTAvis was presented as a potential candidate to serve this purpose