Over the recent years, there has been an explosion of studies on autonomous vehicles. Many collected large amount of data from human drivers. However, compared to the tedious data collection approach, building a virtual simulation of traffic makes the autonomous vehicle research more flexible, time-saving, and scalable. Our work features a 3D simulation that takes in real time position information parsed from street cameras. The simulation can easily switch between a global bird view of the traffic and a local perspective of a car. It can also filter out certain objects in its customized camera, creating various channels for objects of different categories. This provides alternative supervised or unsupervised ways to train deep neural networks. Another advantage of the 3D simulation is its conformation to physical laws. Its naturalness to accelerate and collide prepares the system for potential deep reinforcement learning needs.
Autonomous vehicles have received much attention in recent years. For heuristic approaches, Dresner proposes a reservation-based intersection control mechanism which requires a central controller that collects requests from all vehicles and returns signals of whether they can proceed or not @cite_1 . There are also solutions based on vehicle-to-vehicle and vehicle-to-infrastructure communication devices @cite_2 @cite_5 . However, heuristic approaches requires lots of effort to engineer the rules and make complicated algorithm. As an approximator, neural networks is able to take in images of traffic situations and output decisions. Without the need to enumerate all the rules, training the neural networks are easy and direct. Deep CNNs are now deployed in self-driving cars on highways @cite_4 .
@cite_5 @cite_1 @cite_4 @cite_2
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The automation of driving tasks is of increasing interest for highway traffic management. The emerging technologies of global positioning and intervehicular wireless communications, combined with in-vehicle computation and sensing capabilities, can potentially provide remarkable improvements in safety and efficiency. We address the problem of designing intelligent in-tersections, where traffic lights and stop signs are removed, and cars negotiate the intersection through an interaction of centralized and distributed decision making. Intelligent intersections are representative of complex hybrid systems that are increasingly of interest, where the challenge is to design tractable distributed algorithms that guarantee safety and provide good performance. Systems of automatically driven vehicles will need an under lying collision avoidance system with provable safety properties to be acceptable. This condition raises several challenges. We need to ensure perpetual collision avoidance so that cars do not get into future problematic positions to avoid an immediate collision. The architecture needs to allow distributed freedom of action to cars yet should guard against worst-case behavior of other cars to guarantee collision avoidance. The algorithms should be tractable both computationally and in information requirements and robust to uncertainties in sensing and communication. To address these challenges, we propose a hybrid architecture with an appropriate interplay between centralized coordination and distributed freedom of action. The approach is built around a core where each car has an infinite horizon contingency plan, which is updated at each sampling instant and distributed by the cars, in a computationally tractable manner. We also define a dynamically changing partial-order relation between cars, which specifies, for each car, a set of cars whose worst-case behaviors it should guard against. The architecture is hybrid, involving a centralized component that coordinates intersection traversals. We prove the safety and liveness of the overall scheme. The mathematical challenge of accurately quantifying performance remains as a difficult challenge; therefore, we conduct a simulation study that shows the benefits over stop signs and traffic lights. It is hoped that our effort can provide methodologies for the design of tractable solutions for complex distributed systems that require safety and liveness guarantees. Advances in autonomous vehicles and intelligent transportation systems indicate a rapidly approaching future in which intelligent vehicles will automatically handle the process of driving. However, increasing the efficiency of today's transportation infrastructure will require intelligent traffic control mechanisms that work hand in hand with intelligent vehicles. To this end, Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that by studying the problem from a multiagent perspective, intersection control can be made more efficient than existing control mechanisms such as traffic signals and stop signs. We extend their study beyond the case of an individual intersection and examine the unique implications and abilities afforded by using AIM-based agents to control a network of interconnected intersections. We examine different navigation policies by which autonomous vehicles can dynamically alter their planned paths, observe an instance of Braess' paradox, and explore the new possibility of dynamically reversing the flow of traffic along lanes in response to minute-by-minute traffic conditions. Studying this multiagent system in simulation, we quantify the substantial improvements in efficiency imparted by these agent-based traffic control methods. Recently, several artificial intelligence labs have suggested the use of fully driverless vehicles with the capability of sensing the surrounding environment to enhance the roadway safety. The idea of having fully automated vehicles running in streets was inapplicable for many years until recently when researchers succeeded in releasing fully automated vehicles (without human drivers). Consequently, the paper develops a heuristic optimization algorithm for driverless vehicles at unsignalized intersections using a multi-agent system. The proposed system models the driverless vehicles as autonomous agents controlled by the intersection controller (manager agent). The input information of the system consists of vehicles’ current location, speed and acceleration in addition to the surrounding environment (weather, intersection characteristics, etc.). The intersection controller processes the input information using a built-in simulator: “OSDI” (Optimization Simulator for Driverless vehicles at Intersections). The simulator objective is to optimize the movements of vehicles to reduce the total delay time for the entire intersection and prevent crashes simultaneously. Thereafter, the intersection controller uses the simulator output for controlling the speed profile of the driverless vehicles within the intersection study zone. The proposed system is compared to two different intersection control scenarios: an All-way stop control (AWSC) and an Intersection manager with built-in OSDI. For both scenarios, it is assumed that there are four driverless vehicles (one vehicle per approach) willing to cross a four-legged unsignalized intersection concurrently. The results show using Monte Carlo simulation show that the proposed system reduces the total delay by 35 seconds on average compared to traditional AWSC. This research is considered as a first step in developing an unmanned vehicle technology system. The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study by examining the relationship between the precision of cars' motion controllers and the efficiency of the intersection controller. We propose a planning-based motion controller that can reduce the chance that autonomous vehicles stop before intersections, and show that this controller can increase the efficiency of the intersection control mechanism.