A tractor-trailer parking control scheme using adaptive dynamic programming

This paper studies the online learning control of a truck-trailer parking problem via adaptive dynamic programming (ADP). The contribution is twofold. First, a novel ADP method is developed for systems with parametric nonlinearities. It learns the optimal control policy of the linearized system at the origin, while the learning process utilizes online measurements of the full system and is robust with respect to nonlinear disturbances. Second, a control strategy is formulated for a commonly seen truck-trailer parallel parking problem, and the proposed ADP method is integrated into the strategy to provide online learning capabilities and to handle uncertainties. A numerical simulation is conducted to demonstrate the effectiveness of the proposed methodology.

Izzivi pri odločanju glede načina prevoza na delo

Public transport in Slovenia is poorly organized. The connections and timetables are poor, trains are not as fast, and the savings are not significant if the transportation of choice is a personal automobile. However, this mode of transportation more often than not involves a parking problem. Every company does not have enough parking spaces for all of their employees, which is why some people opt to drive to work together or start from home early to secure a free parking space. We will prepare an overview of the possibilities of transport to work in Slovenia, focusing on the route Ljubljana - Kranj. We will address the issues discussed from the point of view of the mode of transport to work - car, train, bus or even on foot, if the workplace is close to home. Our purpose is to determine whether we would save time and money if we were to travel to work by another means of transport, and to identify other factors that influence our decision regarding the mode of transport to work.

Addressing the “minimum parking” problem for on-demand mobility

Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the “minimum parking” problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.

VEHICLE IDENTIFICATION IN PARKING AREAS USING ADAPTIVE BRIGHTNESS THRESHOLDING

The problem of parking systems on the street is a classic problem that occurs from year to year, manysolutions are offered in solving the parking problem on the street. The problem is not only related to trafficjams due to in and out of vehicles from the parking spaces but also the parking management issues becomepolemic at this time. A prototype of parking management monitoring system tries to provide solution inmanaging parking by using image processing based smart camera. In a prototype the system test performedon day and night conditions, to anticipate the very contrast difference intensity of pixels during the day or night so as to develop vehicle detection program using adaptive brightness thresholding. The results showthat the program has been running quite well to identify vehicles during day and night timeframe

Discretization of the parking problem

The present work consider a natural discretization of R´enyi’s so-called “parking problem”. Let l, n, i be integers satisfying l ≥ 2, n ≥ 0 and 0 ≤ i ≤ n − l. We place an open interval (i, i + l) in the segment [0, n] with i being a random variable taking values 0, 1, 2, . . . , n − l with equal probability for all n ≥ l. If n < l we say that the interval does not fit. After placing the first interval two free segments [0, i] and [i + l, n] are formed and independently filled with the intervals of length l according to the same rule, etc. At the end of the filling process the distance between any two adjacent unit intervals is at most l−1. Let ξn,l denote the cumulative length of the intervals placed. The asymptotics behavior of expectations of the aforementioned random sequence have already been studied. This contribution has an aim to continue this investigation and establish the behavior of variances of the same sequence.

Automatic Parallel Car Parking System using Sensors and Arduino UNO

In this busy world, people are tending towards automation in all routine works which in turn is saving their time. Due to the increased use of cars and congesting places, everywhere we are facing a queue to pass through. One such queue we face is in the parallel parking lots. For solving this problem, many automobile manufacturers have come up with Auto Parking Features in New Model Cars. Then what about Old Cars? Shouldn’t those Old Cars get modified with this Auto Parking facility? Yes, they can get modified with our proposed solution. In this paper, we are presenting a solution in the form of a module for the parallel parking problem called “Automatic Parallel Car Parking System – using Sensors and Arduino UNO”. Along with New Cars, this module can also be integrated with Old Electric Cars to bring Auto Parallel Park feature. This paper also discusses existing Auto Parallel Parking Systems. It also discusses the proposed solution by solving the flaws in existing solutions. The proposed solution is easily adaptable, with small modifications to an electric car. Future enhancements are also proposed.