Trucks

Deep Truck : A Deep Neural Network Model for Longitudinal Dynamics of Heavy Duty Trucks

Albeaik, Saleh
Chou, Fang-Chieh
Lu, Xiao-Yun
Bayen, Alexandre M.
2019

This article demonstrates the use of deep neural networks (NN) and deep reinforcement learning (deep-RL) for modeling and control of longitudinal heavy duty truck dynamics. Instead of explicit use of analytical model derived information or parameters about the truck, the deep NN model is fitted to data using a brief set of historical data collected from an arbitrary driving cycle. The deep model is used in this article to design a cruise controller for the truck using model-free deep-RL. The deep model and the control loop performances are demonstrated both using state-of-the-art...

Longitudinal Deep Truck: Deep Learning and Deep Reinforcement Learning for Modeling and Control of Longitudinal Dynamics of Heavy Duty Trucks

Albeaik, Saleh
Wu, Trevor
Vurimi, Ganeshnikhil
Lu, Xiao-Yun
Bayen, Alexandre
2021

Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from first principles challenging, tedious, and often requires several theoretical and applied areas of expertise to carry through. This article investigates deep learning and deep reinforcement learning as truck-configuration-agnostic longitudinal modeling and control approaches for heavy duty trucks. The article outlines a process to develop and validate such models and...

Integrated Target Tracking and Control for Automated Car-Following of Truck Platforms

Alaskar, Fadwa S
Chou, Fang-Chieh
Flores, Carlos
Lu, Xiao-Yun
Bayen, Alexandre
2022

This article proposes a perception model for enhancing the accuracy and stability of car-following control of a longitudinally automated truck. We applied a fusion-based tracking algorithm on measurements of a single preceding vehicle needed for car following control. This algorithm fuses two types of data, radar and LiDAR data, to obtain more accurate and robust longitudinal perception of the subject vehicle in various weather conditions. The filter's resulting signals are fed to the gap control algorithm at every tracking loop composed by a high-level gap control and lower acceleration...

Deep Truck Cruise Control: Field Experiments and Validation of Heavy Duty Truck Cruise Control Using Deep Reinforcement Learning

Albeaik, Saleh
Wu, Trevor
Vurimi, Ganeshnikhil
Chou, Fang-Chieh
Lu, Xiao-Yun
Bayen, Alexandre M.
2022

Building control systems for heavy duty trucks have historically been dependent on availability of the details of the mechanical configuration of each target truck. This article investigates transfer and robustness of continuous control systems learned using model free deep-RL as an alternative; a configuration agnostic strategy for control system development. For this purpose, deep-RL cruise control policies are developed and validated in simulation and field experiments using two differently configured trucks; full-size Volvo and Freightliner trucks. Their performance are validated for...

Deep Truck Cruise Control: Field Experiments and Validation of Heavy Duty Truck Cruise Control Using Deep Reinforcement Learning

Albeaik, Saleh
Wu, Trevor
Vurimi, Ganeshnikhil
Chou, Fang-Chieh
Bayen, Alexandre M.
2022

Building control systems for heavy duty trucks have historically been dependent on availability of the details of the mechanical configuration of each target truck. This article investigates transfer and robustness of continuous control systems learned using model free deep-RL as an alternative; a configuration agnostic strategy for control system development. For this purpose, deep-RL cruise control policies are developed and validated in simulation and field experiments using two differently configured trucks; full-size Volvo and Freightliner trucks. Their performance are validated for...

The Effects of Truck Driver Wages and Working Conditions on Highway Safety: A Case Study

Rodriguez, DA
Rocha, M
Khattak, AJ
Belzer, MH
2006

The role that human capital and occupational factors play in influencing driver safety outcomes has gained increased attention from trucking firms and policy-makers. This paper examines the role of these factors, in addition to demographic factors, in influencing crash frequency at the driver level. A unique driver-level dataset from a large truckload firm collected over a period of 26 months is used for estimating regression models of crash counts. Based on estimates from a zero inflated Poisson regression model, results suggest that human capital and occupational factors, such as...

Parameter Estimation and Command Modification for Longitudinal Control of Heavy Vehicles

Bae, Hong S.
Gerdes, J. Christian
2003

Commercial heavy vehicles, unlike passenger vehicles, display huge variation in parameters such as vehicle mass. Coupled with lower actuation authorities (engine and brake capabilities), these variations can induce actuator saturation even in moderately demanding maneuvers, presenting challenge to the task of maintaining string stability in a platoon formation of heavy trucks. A new control scheme is proposed to put on-line bounds, or artificial saturation, on command signals via parameter estimation such that all members in a platoon can follow the reference commands without saturating...

Workzone Safety Improvements through Enhanced Warning Signal Devices

Christianson, Kent
Greenhouse, Daniel
Cohn, Theodore
Kim, Roy Young
Chow, Christina
2008

The high incidence of accidents associated with work zones suggests that current warning lights and signals have been in need of improvement. In this project we have developed and tested an improved emergency warning light intended specifically for Caltrans work zone vehicles, and an enhanced rear warning light for shadow trucks, both intended to improve visibility and conspicuity, and to reduce reaction times for drivers approaching the work zone.

Demonstration of Automated Heavy-Duty Vehicles

Shladover, Steve E.
Lu, Xiao-Yun
Song, Bongsob
Dickey, Susan
Nowakowski, Christopher
Howell, Adam
Bu, Fanping
Marco, David
Tan, Han-Shue
Nelson, David
2006

This project was created in order to continue progress toward a future in which vehicle automation technologies are able to improve transportation operations. In the wake of the termination of the National Automated Highway Systems Consortium (NAHSC) program in 1998, the California Department of Transportation (Caltrans) created The Phoenix Project to bring together the organizations that remained interested in this future vision. The discussions within The Phoenix Project focused on the opportunities that could be gained from earlier deployment of automation technologies on transit buses...

Aerodynamic Forces on Truck Models, Including Two Trucks in Tandem

Hammache, Mustapha
Michaelian, Mark
Browand, Fred
2001

The present wind tunnel experiment describes 6-component force and moment data measured for both the cab and the trailer of a simplified model truck. Forces and moments are presented in coefficient form. The cab is sufficiently smooth that no flow separation occurs at zero yaw. The trailer has rounded forward vertical edges and sharp upper and lower edges. Both cab and trailer have wheels. The test matrix includes variation of the cab-trailer gap, and the yaw angle between the model plane of symmetry and the axis of the wind tunnel. The yaw angle is meant to account for the presence of an...