#vector w2

[triumphonlyfan]

VECTOR W2

[mikeepoos-mechanical-mix]

When I first saw images of the Vector W2, sometime late in the '70's it was a revelation. This was a car from the future. Little did I know that a car from the future was going to look like this:

I have never seen a Vector in the flesh, due to their lack of success, and me not actually looking. Though I did see several Lamborghini Countachs which is similar in the futuristic feel, and market aim. As I've grown older the Vectors and Countachs have faded in appeal, The Countach still looks right, especially in it's early phase, but the Vector not so much. Both cars strike me as impossible to get in, and out of, and even see out of. The real cars of the future, have their fair share of abominations, in styling, weight, and too many gadgets. I found that I need something I can get in and out of without having to contorting myself, gets reasonable fuel economy, and still has some things I can do maintenance on myself.

[logankocon1]

GDevelop Platformer Development

For Moon Jumper, the technical basis is the simple platformer game that was outlined in IGB120’s week 2 workshop. This game gave me a really good starting point to building my game, giving it a controllable player character, simple platforming moving mechanics, collisionable platforms, functioning enemies, and art assets. A lot of this is useful for the game I want to create, outlined in my Elevator Pitch post. 

The first thing I did was update some of the assets to create a visual style more in-line with my vision. Some of the assets, especially the astronaut player character from the tutorial assets, were fitting enough. I created a black background image with white dots scattered about it as the backdrop of space and found a cartoonish moon texture that I could use as the tiling platform texture. 

Once I had a quick, clean visual upgrade, I began working on creating the movement mechanics. Since I knew I wouldn’t have much time for this particular game, rather than adding a bunch of different features and completely fleshing the game out, I opted to treat it as a gameplay prototype for the movement mechanics. I used some of the information from chapter 8 of the textbook, which details digital prototyping, as a basis for this. I knew my control scheme wouldn’t change, and the game wouldn’t need major technical prototyping, so I focused mainly on gameplay mechanics and kinesthetics. 

The first thing I did was add a simple “bouncing” mechanic, making it so whenever the player came into collision with a platform, it would simulate the jump button being pressed. By also lowering the gravity of the player character in the behaviors tab, I accomplished the bouncing and floatiness feeling I was looking for. I could even control the player slightly by moving left and right. 

Finally, I made it so that when the player hits “space”, a strong force is applied downward, shooting the player towards the ground, giving them more control over the movement. This was the basis for my movement, but I knew it would require a lot of iteration. To playtest this simple movement, I built a small level, placing platforms and monsters down. This allowed me to change my mechanics, mostly by changing values like the amount of gravity, jump speed and time, and the force applied when pressing “space.” By playing the game and making changes accordingly, I was able to get the movement to a place that I think is pretty fun. However, I believe the game requires more playtesting to figure out what features to change and add and how to fully fine-tune my movement.

Final movement result. As you can see, the current mechanics allow the player to bounce off the side of platforms, which, in my playtesting, was quite fun.

Application of Fullerton Textbook Theory

While developing this platformer, I had chapter 1 of the textbook firmly in mind, especially the ideas of chasing the fun and advocating for the player. I continuously changed designs and tweaked my prototype to make the player character's movement as fun and responsive as possible. A critical part of the chapter I kept in mind was “Setting player experience goals” In particular, I wanted the player to feel like their control over the character was unique and floaty. By decreasing gravity, making the player near weightless, I achieved the “floaty” feeling I sought. And by playtesting my game and tweaking movement variables, I was able to make that feeling fun.

Fullerton, T. (2018). Game Design Workshop: A Playcentric Approach to Creating Innovative Games. ProQuest Ebook. Retrieved from: https://ebookcentral.proquest.com/lib/qut/reader.action?docID=5477698

Moon texture retrieved from https://www.shutterstock.com/image-vector/moon-surface-texture-beautiful-banner-wallpaper-636994813

Other assets retrieved from IGB120 Workshop 2 https://canvas.qut.edu.au/courses/21200/pages/igb120-w2-workshop-introduction-to-gdevelop-platformers?module_item_id=1928114

[playstationvii]

AI Data learning and Data management:

Learning and Integration of predictive algorithms for information subsequent subsets, data storage and advanced AI learning:

Sure, here are some of the algorithms I am using in my project:   Linear regression: This is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The equation for linear regression is as follows: y = β0 + β1x1 + β2x2 + … + βnxn where y is the dependent variable, x1, x2, …, xn are the independent variables, and β0, β1, β2, …, βn are the coefficients of the model.

Support Vector Machines (SVM): SVM is a supervised learning algorithm that can be used for both classification and regression tasks. The algorithm works by finding the hyperplane that best separates the data points in the dataset. The equation for SVM is as follows: y = f(x) = w^T x + b where w is the weight vector, x is the input vector, and b is the bias term.

Decision Trees: Decision trees are a type of supervised learning algorithm that can be used for classification and regression tasks. The algorithm works by splitting the dataset into subsets based on the values of the features, and then it creates a tree-like structure that maps the decisions that need to be made to reach the desired outcome. The equation for decision trees is as follows: y = f(x) = w1x1 + w2x2 + … + wnxn where y is the outcome, w1, w2, …, wn are the weights, and x1, x2, …, xn are the features.

Naive Bayes: Naive Bayes is a probabilistic classification algorithm that is based on Bayes' theorem. The algorithm works by assuming that the features are conditionally independent, which means that the value of one feature does not depend on the value of any other feature. The equation for Naive Bayes is as follows: P(y|x) = P(x|y)P(y)/P(x) where P(y|x) is the probability of y given x, P(x|y) is the probability of x given y, P(y) is the prior probability of y, and P(x) is the marginal probability of x.

K-Nearest Neighbors (KNN): KNN is a non-parametric classification algorithm that works by finding the k closest neighbors of a point in the dataset and using their labels to predict the label of the point. The equation for KNN is as follows: y = f(x) = 1/k * (w1y1 + w2y2 + … + wkyk) where y is the predicted label, k is the number of neighbors, w1, w2, …, wk are the weights, and y1, y2, …, yk are the labels of the k closest neighbors.

Random Forest: Random Forest is an ensemble learning algorithm that combines multiple decision trees to create a more accurate and robust model. The algorithm works by creating multiple decision trees, each with a random subset of the features, and then using the majority vote of the trees to make a prediction. The equation for Random Forest is as follows: y = f(x) = 1/n * (f1(x) + f2(x) + … + fn(x)) where y is the predicted label, n is the number of trees, and f1, f2, …, fn are the individual decision trees.

Neural Networks: Neural networks are a type of artificial intelligence that is inspired by the structure and function of the human brain. The algorithm works by using a network of interconnected nodes, or neurons, to process input data and make predictions. The equation for neural networks is as follows: y = f(x) = w1 * f(w2x1 + w3x2 + … + wnxn) + b where y is the predicted label, w1, w2, …, wn are the weights, x1, x2, …, xn are the input features, and b is the bias term.

[the-firebird69]

Vector W2 pt2

This is what I mean this car is very hot and it's not when this in production is a kit car and we can make it quite easily and there would be opposition and because these people are picky and really they're not doing much they do it in some in the cities and yeah 30% of it in the old cities there famous probably shouldn't try and make this one that's really cool

Hera

Zues

I think we should it's also extremely fast it's faster than almost everything out there he's going to look up the company you'll be surprised about it but I think I'm going to put this in the pipe and he can build a kit and he was surprised what car and he says the new charger that's probably right yes so c3 it's faster than hell

Thor Freya

Olympus we have to put a serious limiter and it would be mid-engine and high speed and high quality and BG is going to have his own design on his version and it's like his others yes

[ux-jobs]

#uxjobs | Content Creative, Design https://www.behance.net/joblist/303105/Content-Creative-Design?utm_source=dlvr.it&utm_medium=tumblr

[rickyreeves1980]

Formula One: Built to Win is a 1990 racing video game for the Nintendo Entertainment System developed by Winky Soft and published by SETA Corporation.

It was one of the first racing games to feature a career mode, multiple vehicles and an opportunity for the player to increase the performance of their in-game car through car tuning, which were unique elements for a racer of the NES era and a genre only truly revisited during the fifth generation of game consoles where games such as Gran Turismo became popular.

Races start out as single-lap events but become double-lap events as the player starts racing against more experienced competition in places like Las Vegas, Nevada and Hawaii. With gameplay similar to Rad Racer and Pole Position, the driver races towards the back of the screen.

The names of the opponents are chosen partially at random; they can also vary because of the ranking level of the course and the type of vehicle used. Like in Rad Racer, the player can supercharge an automobile to go up to 255 miles per hour or 410 kilometres per hour.

Once the player enters a user name, the player has to start out driving a Mini Cooper without an international racing license. From there, the player must earn the rankings needed to get better performing vehicles and automobile parts. These cars consist of the Vector W2, the Ferrari F40 and finally a Formula-1 race car; most of the game is driven with these vehicles.

Drivers must also be prepared to negotiate their way through both civilian traffic (green) and other racers (blue), both of which resemble whatever car that the player is driving at that time; for example, the Mini Cooper-based opponents appear to be 1990 Volkswagen Beetles.

Managing a racing career

The player starts in New York and must work towards the West Coast. All of tracks that make up the Formula One portion of the game are actual Formula One tracks from around the world. Elements from the 1990 and 1991 Formula One seasons have been used for this video game.

Because this game is based on the career of a race car driver who is trying to go from "Rags to Riches," the beginning of the game provides a slow and conservative period. Designing an unbeatable car from the parts that they purchased in parts shops helps improve the handling, acceleration, braking, and maximum speed of their vehicle. It is possible to develop millionaire or even multimillionaire race car drivers by being successful at the slot machine game at the Las Vegas portion of the game which may be visited at any time. Even though the player must manually turn his or her entire bank account into casino tokens, the casino tokens are automatically turned back into "dollars" after the player leaves the casino.

The player must earn money in order to keep racing; this is done by either winning races or finishing in podium position. After winning the race, the money must be invested in faster and/or more efficient parts to improve the performance of the vehicle. Having a more efficient racing vehicle will eventually result in winning more difficult races where the winner's prize is higher than in the easier races. Eventually, winning certain races will result in acquiring an international racing license. Parts in the game vary in cost and tires can only be used a limited number of times before they have to be purchased again. Tires that have the worst handling can be used an unlimited number of times. These tires are the only automobile parts that are free and the player automatically starts out with these at the beginning of the game's "career mode."

Other modes

In addition to the career (normal) mode of the game, there is also a "free mode" of the game that allows unlimited use of all four vehicles on their respective tracks. These races are done without the distraction of other vehicles.

All races in "free mode" are single-lap only. There is still a limited amount of nitro like in the "normal mode" of the game. However, all races done under "free mode" always start with the nitro gauge filled up. New time records made in the "free mode" are saved into the game's battery along with the driver's name until they are broken by another player. Once the player reaches the Formula One level, he or she must race against drivers whose names sound similar to the actual drivers of the 1990 and 1991 Formula One seasons. For example, Satoru Nakajima is known in the game as S.Nakazma.

Flag Race Track

Japan Suzuka Circuit

France Paul Ricard

Canada. Circuit Gilles Villeneuve

Mexico Autodromo Hermanos Rodriguez

United States Phoenix street circuit

Spain Jarama

United Kingdom Silverstone Circuit

Monaco Monaco

[silhouettehistory]

Vector SilhouetteHistory

Silhouettes of Vector supercars, inlcuding an early form of W2, a W8, the Avtech WX3 Concept, an M12 and a WX8 prototype

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[fuzzyghost]

[yodaprod]

Vector W2

[iamsoretro]

Targa Audio Advert Featuring the Vector W2 

[topvehicles]

Vector W2

via reddit

[thundertruck-blog]

source: http://www.gatsbyonline.com/automobile/vector-lamborghini-ferrari-350709/

[science70]

Vehicle Design Force Vector W2 mock-up, 1976.

[newretrowave]

Vector W2 Twin Turbo

[mocprod]