Presence Of Emergency Vehicles Research Articles

Safe and efficient manoeuvring for emergency vehicles in autonomous traffic using multi-agent proximal policy optimisation

Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which cannot cover all possible scenarios that can take place in autonomous traffic. Multi-Agent Proximal Policy Optimisation (MAPPO) has recently emerged as a powerful method for autonomous systems because it allows for training in thousands of different situations. In this study, we present an approach based on MAPPO to guarantee the safe and efficient manoeuvring of autonomous vehicles in the presence of an emergency vehicle. We introduce a risk metric that summarises the potential risk of collision in a single index. The proposed method generates cooperative policies allowing the emergency vehicle to go at higher average speed while maintaining high safety distances. Moreover, we conduct a comprehensive evaluation of our method in a wide range of scenarios, including assessing the trade-offs between traffic efficiency and safety, measuring the scalability of the approach with respect to the number of autonomous vehicles, analysing different distributions of mixed human and autonomous traffic, and examining the various levels of cooperation and competition among agents.

Improvement of Multi-agent Routing Guidance with an Intelligent Traffic Light Scheduling and the Ability to Select Intermediate Destinations

Traffic congestion and route guidance are integral parts of urban development in large cities. How cars are routed and the traffic flowing have a direct impact on each other. Therefore, the first step is to determine a criterion for assessing the traffic situation. The type of vehicles should also be considered in routing. Emergency vehicles must arrive at their mission site as soon as possible. Public transportations must also travel according to their plans. Ordinary vehicle drivers can either choose a road as an intermediate destination (out of interest, pick up someone, etc.). In this paper, two new algorithms are proposed to 1) route with intermediate destination selection for ordinary vehicles, and 2) schedule traffic lights to decrease traffic density and routing delay. The first algorithm proposed an agent-based route guidance model that in addition to finding the least expected travel time (LET) routes, drivers could select a part of the route as intermediate destinations according to their interests, to raise their satisfaction level. The second algorithm considers the density of traffic flow and the presence of emergency vehicles. This algorithm evaluates the status of the traffic flow by fuzzy logic. The evaluation is conducted by considering traffic flow speed and density. The output of fuzzy logic is used by the Gradational Search Algorithm (GSA). GSA regards the status of the flow, the priority of the traffic flow, and the distance of the emergency vehicles to the traffic light. The simulation results prove that the proposed algorithms have better performances.

An Enhanced 0–1 Mixed-Integer LP Formulation for Traffic Signal Control

An enhanced 0-1 mixed-integer linear programming formulation based on the cell-transmission model is proposed for the traffic signal optimization problem. This formulation has several features that are currently unavailable in other existing models developed with a similar approach, including the components for handling the number of stops, fixed or dynamic cycle length and splits, and lost time. The problem of unintended vehicle holding, which is common in analytical models, is explicitly treated. The formulation can be utilized in developing strategies for adaptive traffic-control systems. It can also be used as a benchmark for examining the convergence behavior of heuristic algorithms based on the genetic algorithm, fuzzy logic, neural networks, or other approaches that are commonly used in this field. The discussion of extending the proposed model to capture traffic signal preemption in the presence of emergency vehicles is given. In terms of computational efficiency, the proposed formulation has the least number of binary integers as compared with other existing formulations that were developed with the same approach.