Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

Abstract

According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity.

How Do Autonomous Vehicles Decide?

“…The feasible and collision-free trajectories were planned using the nonlinear MPC. Following this, Lin et al [ 43 ] also implemented a safe overtaking maneuver for an AV based on time-to-lane crossing estimation and the MPC scheme. Viana et al [ 44 ] also proposed the non-linear MPC to solve the problem of cooperative optimal trajectory generation for AD.…”

Design and Analysis of the Trajectory of an Overtaking Maneuver Performed by Autonomous Vehicles Operating with Advanced Driver-Assistance Systems (ADAS) and Driving on a Highway

“…The AVs also have numerous advantages, such as reducing traffic congestion, reducing fuel consumption, improving public transportation services, and facilitating maneuvers [10]. The application of new technologies in maneuvers implies that interactions between AVs and environmental sensing may improve safety, efficiency and sustainability [11,12].…”

Predicting Overtakes In Trucks Using Can Data

“…Lane changes, acceleration and deceleration, and estimation of the speed and distance of the vehicle ahead or in the lane it is travelling in are all part of the process. Though there is a lot of work in the literature that aims at predicting driving manoeuvres, very few address overtaking [2,3,4], and no realworld dataset is available due to the risk associated with overtaking [5]. Most works address the estimation of lane change [1] or turning intention at intersections [6].…”