Topic Tag: car

home Forums Topic Tag: car

 Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models

 

In the event of sensor failure, it is necessary for autonomous vehicles to safely execute emergency maneuvers while avoiding other vehicles on the road. In order to accomplish this, the sensor-failed vehicle must predict the future semantic behaviors of other drivers, such as lane changes, as well …


 Clusters of Driving Behavior from Observational Smartphone Data

Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. O…


 Conversational AI: The Science Behind the Alexa Prize

  

Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar un…


 DeepTraffic: Driving Fast through Dense Traffic with Deep Reinforcement Learning

  

We present a micro-traffic simulation (named “DeepTraffic”) where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make t…


 Mercedes-Benz Greener Masking Challenge Masking Challenge–1st Place Winner’s Interview

 

To ensure the safety and reliability of each and every unique car configuration before they hit the road, Daimler’s engineers have developed a robust testing system. But, optimizing the speed of their testing system for so many possible feature combinations is complex and time-consuming without a…


 Learning to Customize Network Security Rules

 

Security is a major concern for organizations who wish to leverage cloud computing. In order to reduce security vulnerabilities, public cloud providers offer firewall functionalities. When properly configured, a firewall protects cloud networks from cyber-attacks. However, proper firewall configura…


 An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems

As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of …


 Building Robust Deep Neural Networks for Road Sign Detection

   

Deep Neural Networks are built to generalize outside of training set in mind by using techniques such as regularization, early stopping and dropout. But considerations to make them more resilient to adversarial examples are rarely taken. As deep neural networks become more prevalent in mission-crit…


 A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and…


 Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor

 

A novel predictor for traffic flow forecasting, namely spatio-temporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial and temporal information available in a transportation network to carry our traffic flow forecasting of the current site…


 Carvana Image Masking Challenge–1st Place Winner’s Interview

  

This year, Carvana, a successful online used car startup, challenged the Kaggle community to develop an algorithm that automatically removes the photo studio background. This would allow Carvana to superimpose cars on a variety of backgrounds. In this winner’s interview, the first place team…


 Evaluation of Speech for the Google Assistant

     

Posted by Enrique Alfonseca, Staff Research Scientist, Google Assistant Voice interactions with technology are becoming a key part of our lives — from asking your phone for traffic conditions to work to using a smart device at home to turn on the lights or play music. The Google Assistant is desi…


 A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms

We propose a two phase time dependent vehicle routing and scheduling optimization model that identifies the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting rou…


 Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue

 

We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data collected will be used to develop a dialogue system for robot navigation. Building on an interface previously used in the de…


 Distributed algorithm for empty vehicles management in personal rapid transit (PRT) network

In this paper, an original heuristic algorithm of empty vehicles management in personal rapid transit network is presented. The algorithm is used for the delivery of empty vehicles for waiting passengers, for balancing the distribution of empty vehicles within the network, and for providing an empt…


 Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control

  

Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic. Leveraging recent advances in deep Reinforcement Learning (RL), Flow enables …


 On a Formal Model of Safe and Scalable Self-driving Cars

In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of sa…


 Towards lightweight convolutional neural networks for object detection

 

We propose model with larger spatial size of feature maps and evaluate it on object detection task. With the goal to choose the best feature extraction network for our model we compare several popular lightweight networks. After that we conduct a set of experiments with channels reduction algorithm…


 GaDei: On Scale-up Training As A Service For Deep Learning

   

Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user’s dataset is prohi…


 Creating a Social Brain for Cooperative Connected Autonomous Vehicles: Issues and Challenges

The connected autonomous vehicle has been often touted as a technology that will become pervasive in society in the near future. Rather than being stand alone, we examine the need for autonomous vehicles to cooperate and interact within their socio-cyber-physical environments, including the problem…


 DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

   

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affect…


 Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

 

Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these a…


 DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

   

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining roa…


 Traffic Optimization For a Mixture of Self-interested and Compliant Agents

This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents ar…


 A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning

  

Resource allocation is still a difficult issue to deal with in wireless networks. The unstable channel condition and traffic demand for Quality of Service (QoS) raise some barriers that interfere with the process. It is significant that an optimal policy takes into account some resources available …