pomdps.jl

MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.

python-can

The can package provides controller area network support for Python developers

pythonpilot

An Open Source Python Framework for Prototyping ADAS and Autonomous Vehicles

pythonrobotics

Python sample codes for robotics algorithms.

q-learning-based-smart-cab

Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:  On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.  On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.

realtimepathplanning

Sampling based rewiring approaches to solve motion planning problems for a robot with dynamic obstacles

reinforcement-learning

Reinforcement learning baseline agent trained with the Actor-critic (A3C) algorithm.

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

reinforcement-learning-for-decision-making-in-self-driving-cars

Reinforcement-Learning-for-Decision-Making-in-self-driving-cars

research-method

论文写作与资料分享

rsmotion

RSMotion - C++ Library for Reeds-Shepp Cars

runroader

Speed planning with convex optimization

self-driving-cars

Coursera Open Courses from University of Toronto

self-driving-vehicle

Simulation of self-driving vehicles in Unity. This is also an implementation of the Hybrid A* pathfinding algorithm which is useful if you are interested in pathfinding for vehicles.

spatiotemporal_semantic_corridor

Implementation of the paper "Safe Trajectory Generation For Complex Urban Environments Using Spatio-temporal Semantic Corridor".

steering_functions

temporaloptimization

Speed profile planning via temporal optimization

trajectory_planning

Planning of longitudinal trajectories for self driving cars

trajectory_planning_helpers

Useful functions used for path and trajectory planning at TUM/FTM