Sim-to-Real Autonomous Vehicle Lane Keeping using Vision

2021 ◽  
Author(s):  
Jelena Kocic ◽  
Nenad Jovicic
Keyword(s):  
Autonomous Vehicle ◽  
Lane Keeping

Combined Lane Keeping and Longitudinal Speed Control for Autonomous Driving

2019 ◽  
Author(s):  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Control Method ◽  
Autonomous Vehicle ◽  
Speed Control ◽  
Autonomous Driving ◽  
External Input ◽  
The Road ◽  
Longitudinal Dynamic ◽  
Bicycle Model ◽  
Lane Keeping ◽  
Yaw Angle

Abstract This paper presents a technique for the lane keeping and the longitudinal speed control of an autonomous vehicle with the combination of an MPC and a PID control. The goal of the proposed control method is to minimize the lateral deviation and relative yaw angle with respect to the planned trajectory, while driving the vehicle at the highest acceptable longitudinal speed. The reference profile of the longitudinal speed is computed considering both the lateral and longitudinal dynamic of the vehicle. The vehicle is represented by means of a linear 3-DoF bicycle model. The control algorithm takes the road lane boundaries as the only external input. The proposed strategy is validated in simulation on three distinct driving scenarios.

Behavioral Adaptations to Lane Keeping Systems: Effects of Exposure and Withdrawal

2018 ◽  
Vol 61 (1) ◽  
pp. 152-164 ◽  
Author(s):  
Erika E. Miller ◽  
Linda Ng Boyle
Keyword(s):  
Negative Binomial ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Lateral Position ◽  
Driving Performance ◽  
Performance Standard ◽  
Cognitive Workload ◽  
Behavioral Adaptations ◽  
Vehicle Systems ◽  
Lane Keeping

Objective: A driving simulator study was conducted to evaluate the longitudinal effects of an intervention and withdrawal of a lane keeping system on driving performance and cognitive workload. Background: Autonomous vehicle systems are being implemented into the vehicle fleet. However, limited research exists in understanding the carryover effects of long-term exposure. Methods: Forty-eight participants (30 treatment, 18 control) completed eight drives across three separate days in a driving simulator. The treatment group had an intervention and withdrawal of a lane keeping system. Changes in driving performance (standard deviation of lateral position [SDLP] and mean time to collision [TTC]) and cognitive workload (response time and miss rate to a detection response task) were modeled using mixed effects linear and negative binomial regression. Results: Drivers exposed to the lane keeping system had an increase in SDLP after the system was withdrawn relative to their baseline. Drivers with lane keeping had decreased mean TTC during and after system withdrawal compared with manual drivers. There was an increase in cognitive workload when the lane keeping system was withdrawn relative to when the system was engaged. Conclusion: Behavioral adaptations in driving performance and cognitive workload were present during automation and persisted after the automation was withdrawn. Application: The findings of this research emphasize the importance to consider the effects of skill atrophy and misplaced trust due to semi-autonomous vehicle systems. Designers and policymakers can utilize this for system alerts and training.

scholarly journals Design of Lane Keeping Algorithm of Autonomous Vehicle

2016 ◽  
Vol 44 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Olivér Törő ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Autonomous Vehicle ◽  
Lane Keeping

scholarly journals Neural Network Based Robust Lateral Control for an Autonomous Vehicle

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 510
Author(s):  
Subrat Kumar Swain ◽  
Jagat J. Rath ◽  
Kalyana C. Veluvolu
Keyword(s):  
Neural Network ◽  
Environmental Conditions ◽  
High Frequency ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
External Disturbances ◽  
High Frequency Oscillations ◽  
Equivalent Control ◽  
Adverse Environmental Conditions ◽  
Lane Keeping

The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is considered to generate a robust control signal under uncertainties. However, conventional SMC suffers from the issue of high frequency oscillations, called chattering. To address the issue of chattering and reduce the effect of unknown external disturbances in the absence of precise model information, a radial basis function neural network (RBFNN) is employed to estimate the equivalent control. Further, a higher order sliding mode (HOSM) based switching control is proposed in this paper to compensate for the effect of external disturbances. The effectiveness of the proposed controller in terms of lane-keeping and lateral stability is demonstrated through simulation in a high-fidelity Carsim-Matlab Simulink environment under a variety of road and environmental conditions.

scholarly journals Randomized Adversarial Imitation Learning for Autonomous Driving

2019 ◽  
Author(s):  
MyungJae Shin ◽  
Joongheon Kim
Keyword(s):  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Imitation Learning ◽  
Cruise Control ◽  
Driving System ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Multi Agent ◽  
Lane Keeping ◽  
Autonomous Driving System

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical. The autonomous driving system simultaneously activates multiple ADAS functions; and thus it is essential to coordinate various ADAS functions. This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors. The RAIL policies are trained through derivative-free optimization for the decision maker that coordinates the proper ADAS functions, e.g., smart cruise control and lane keeping system. Especially, the proposed method is also able to deal with the LIDAR data and makes decisions in complex multi-lane highways and multi-agent environments.

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

2021 ◽  
Author(s):  
Safwan Ghanem ◽  
Priyadarshi Kanungo ◽  
Ganapati Panda ◽  
Suresh Chandra Satapathy ◽  
Rohit Sharma
Keyword(s):  
Smart Cities ◽  
Autonomous Vehicle ◽  
Real Life ◽  
Warning System ◽  
Lane Detection ◽  
Vehicle Localization ◽  
Lane Departure ◽  
Time Requirements ◽  
Remote Controller ◽  
Lane Keeping

AbstractLane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

Sign in / Sign up

Export Citation Format

Share Document