In this paper we describe a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit, a Velodyne three-dimensional lidar scanner, two push-broom forward-looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research Campus and downtown Dearborn, MI, during November-December 2009. The vehicle path trajectory in these data sets contains several large- and small-scale loop closures, which should be useful for testing various state-of-the-art computer vision and simultaneous localization and mapping algorithms.

/pdf/ford-campus-vision-and-lidar-data-set-1vigskldzy.pdf

Ford Campus vision and lidar data set

Safe driving envelopes for path tracking in autonomous vehicles

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems U+0028 CPSS U+0029 framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space, considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon U+0028 iHorizon U+0028 and its applications are also presented towards parallel horizon. The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.

/pdf/parallel-driving-in-cpss-a-unified-approach-for-transport-5231s3eeih.pdf

Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence

In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem. The optimization problem is solved by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-varying system constraints in a receding horizon fashion. Formulating the system dynamics linearly in combination with a quadratic cost function has two great advantages. First, the system constraints can be set up not only to achieve collision avoidance with both static and dynamic obstacles, but also aspects of human driving behavior can be considered. Second, the optimization problem can be solved very efficiently, such that the algorithm can be run with little computational effort. In addition, due to an elaborate problem formulation, reference curves with discontinuous, high curvatures will be effortlessly smoothed out by the algorithm. This makes the proposed algorithm applicable to different traffic scenarios, such as parking or highway driving. Experimental results are presented for different real-world scenarios to demonstrate the algorithm’s abilities.

Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC

Extrinsic Calibration of a 3D Laser Scanner and an Omnidirectional Camera

A vehicle system includes at least one autonomous driving sensor that detects a location of a target vehicle, a communication device that receives infrastructure information from an infrastructure device, and a processing device that controls operation of at least one vehicle subsystem according to the infrastructure information. An exemplary method includes determining a location of a target vehicle, receiving infrastructure information from an infrastructure device, and controlling operation of at least one vehicle subsystem according to the infrastructure information.

Vehicle-to-infrastructure communication

This paper reports on various aspects of the Intelligent Vehicle Systems (IVS) team's involvement in the recent 2007 DARPA Urban Challenge, wherein our platform, the autonomous “XAV-250,'' competed as one of the 11 finalists qualifying for the event. We provide a candid discussion of the hardware and software design process that led to our team's entry, along with lessons learned at this event and derived from participation in the two previous Grand Challenges. In addition, we give an overview of our vision-, radar-, and LIDAR-based perceptual sensing suite, its fusion with a military-grade inertial navigation package, and the map-based control and planning architectures used leading up to and during the event. The underlying theme of this article is to elucidate how the development of future automotive safety systems can potentially be accelerated by tackling the technological challenges of autonomous ground vehicle robotics. Of interest, we will discuss how a production manufacturing mindset imposes a unique set of constraints upon approaching the problem and how this worked for and against us, given the very compressed timeline of the contests. © 2008 Wiley Periodicals, Inc.

/pdf/a-perspective-on-emerging-automotive-safety-applications-2kl1hkjdi9.pdf

A perspective on emerging automotive safety applications, derived from lessons learned through participation in the DARPA Grand Challenges

A Perspective on Emerging Automotive Safety Applications, Derived from Lessons Learned through Participation in the DARPA Grand Challenges.

A controllable suspension system includes air springs and variable damping shock absorbers (108) switchable between first and second effective spring rates and soft and firm damping rates. At speeds above a given speed, lateral accelerations exceeding predetermined limit will result in switching the shock absorbers to firm and closing front and rear gate valves (126,128) between left and right air springs to increase the spring rate from a first effective spring rate to a second effective spring rate to increase vehicle tilt stabilization.

Pneumatic tilt stabilization suspension system

In vehicles having drive wheel selections, accessed for example through a transfer case, drive wheel settings are used to determine a desired vehicle ride height at which the vehicle is maintained by a vehicle suspension control system. Preferably, both vehicle speed and transfer case setting are combined to determine the desired vehicle ride height. With two wheel drive selected, the desired vehicle ride height is set to a low ride height which provides the most aerodynamic airflow for the vehicle. However, if four wheel drive is selected, the speed is checked to determine the desired vehicle ride height. If four wheel drive (automatic four wheel drive) is selected, low ride height is still selected for speeds in excess of a first defined speed. For speeds at or below the first defined speed, a high ride height approximately 1 inch above the low ride height is set for the desired ride height. If four wheel drive low is selected, typically in contemplation of off-road operation, a higher still ride height or off-road ride height approximately 1 inch above high ride height is set for the desired ride height.

Ride height setting based on transfer case setting

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