DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. To demonstrate this, we train a deep Convolutional Neural Network using recording from 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments. We also train a model for car distance estimation on the KITTI dataset. Results show that our direct perception approach can generalize well to real driving images. Source code and data are available on our project website

On the properties of Gaussian Copula Mixture Models

Gaussian copula mixture models (GCMM) are the generalization of Gaussian Mixture models using the concept of copula. Its mathematical definition is given and the properties of likelihood function are studied in this paper. Based on these properties, extended Expectation Maximum algorithms are developed for estimating parameters for the mixture of copulas while marginal distributions corresponding to each component is estimated using separate nonparametric statistical methods. In the experiment, GCMM can achieve better goodness-of-fitting given the same number of clusters as GMM; furthermore, GCMM can utilize unsynchronized data on each dimension to achieve deeper mining of data.Comment: 11 pages paper for theoretical properties and new algorithms for GCM

GPS-Based Highway Performance Monitoring Performance Monitoring Using GPS: Characterization of Travel Speeds on any Roadway Segment

Presented is a characterization of travel speed on any roadway segment based on probe vehicle position data. Most of the characterization is based position data obtained from GPS receivers because of their high precision and their increasing availability. Comparison is also made to Qualcomm’s Automatic Satellite Position Reporting (QASPR) system because of its long history (10+ years) of extensive use by the long-haul trucking industry. Described is the use of these data in conjunction with digital map representations of roadways with particular reference to ALK’s digital map database of North America. Two examples of the use of probe vehicle based GPS data to ascertain and monitor speed on roadway segments are presented. One is a demonstration of the monitoring of the speed performance of the various road segments that make up the Québec-Windsor corridor. Extensive GPS data from the first half of 2008 characterize the speed performance of the corridor by day-of-week and time-of-day. The second example also uses GPS probe vehicle data to assign a median speed , by direction, to all 31 million arcs of ALK’s digital map database of North America. Examples of that assignment are displayed in geographic bandwidth charts and a generic example of a fastest route computed based on the assigned median speeds is presented

Arroyo, S.

In transportation networks, it is more useful to think of costs on links as travel times as opposed to distances. Furthermore, while distances are usually constant, deterministic values, between nodes, travel times often vary substantially, as a result of incidents, road conditions, weather, traffic volume and drivers ' preferences, among others. Recent research has developed a variety of algorithms for routing in these non-deterministic networks, but less has been done in identifying the proper functional forms to describe these travel times distributions. Moreover, most of the routing algorithms rely on the assumption that travel time distributions are independent random variables between links. In this paper, recently available data obtained from drivers using in-vehicle route guidance systems is used to empirically analyze the behavior of travel times on the US road network. Normal, lognormal, gamma and Weibull distributions are fitted to these travel times and it is concluded that the lognormal model provides the better fit. The data is then used to test the assumption of independence between arcs. A road segment comprised of eight links is selected and the correlation between travel times on the links is obtained. The correlations for consecutive links are compared, as well as for links separated by one or more links. The issue of convoluting travel time distributions when these times are not independent is analyzed. For this purpose, Reciprocal Gamma distributions are used, which have been proven to represent the infinite sum of correlated lognormal distributions

The Effect of Augmented Driver Behavior on Freeway Traffic Flow

This paper investigates the possible virtue of the modification of longitudinal and lane-change behaviors of drivers by intelligent cruise control systems that augment individual driver behavior by enforcing minimum separation between vehicles. Such systems would not only reduce collisions but may also improve traffic flow by reducing the frequency of bottlenecks on freeways. This hypothesis is tested using a modified microsimulation of a length of freeway in Los Angeles County. A transit-oriented minimum time headway controller is compared to a traditional minimum separation intelligent cruise controller. The results show that using a fixed distance policy to control the separation tends to keep the flow more stable during peak periods and reduces travel times

Analysis, Characterization, and Visualization of Freeway Traffic Data in Los Angeles

Presented is an analysis of a large volume of readily available loop detector based traffic data for the Los Angeles and Ventura Counties. The data suggests that the daily temporal variation of congestion along any directional road segment can be characterized quite well by a 10-parameter function. The function is shown to be suitable for use in the classification of road segments, such as having morning but no afternoon or evening congestion, as well as for the purpose of improving real-time forecasts of congestion ahead for use in generating dynamic real-time minimum estimated time-of-arrival turn-by- turn navigation instructions. Automation of the process allows for the characterization of all of the Los Angeles and Ventura Counties and can be applied to any metropolitan area having similar data. Several interactive and dynamic visualization tools using Google Earth are also developed and presented