scholarly journals Driving and steering collision avoidance system of autonomous vehicle with model predictive control based on non-convex optimization

2021 ◽  
Vol 13 (6) ◽  
pp. 168781402110276
Author(s):  
Yuho Song ◽  
Kunsoo Huh
Keyword(s):  
Convex Optimization ◽  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Lane Changes ◽  
Vehicle Communication ◽  
Rapid Control ◽  
Rapid Control Prototyping ◽  
Predictive Controller ◽  
Collision Avoidance System

A planar motion control system is proposed for autonomous vehicles not only to follow the lanes, but also to avoid collisions by braking, accelerating, and steering. The supervisor is designed first to determine the desired speed and the risk of the maneuvering due to road boundaries and obstacles. In order to allow lane changes on multi-lane roads, the model predictive controller is formulated based on the probabilistic non-convex optimization. The micro-genetic algorithm is applied to calculate the target speed and target steering angle in real time. A software-in-the-loop unit is constructed with the Rapid Control Prototyping device in the vehicle communication environment. The performance of the proposed system is verified for various collision avoidance scenarios and the simulation results demonstrate the safe and effective driving performance of autonomous vehicles with no collision on multi-lane road.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

Experimental Vehicle That Avoids Collisions Through Steer by Wire and Differential Drive Systems

2011 ◽  
Author(s):  
Victor J. Gonzalez-Villela ◽  
Eduardo U. Gonzalez-Zavala
Keyword(s):  
Collision Avoidance ◽  
High Speed ◽  
Autonomous Vehicle ◽  
Infrared Sensors ◽  
Differential Drive ◽  
Steer By Wire ◽  
Experimental Vehicle ◽  
Collision Avoidance System ◽  
Drive Systems ◽  
Wire System

The implementation of Drive-by-Wire systems is increasing due to their advantages. One of these advantages is the capability to be autonomous or semiautonomous. This paper investigates the collisions avoidance in a Steer-by-Wire and Differential Drive experimental vehicle. The Steer-by-Wire system is tested using the Ackerman formulation. Ackerman equations are modified in order to vary the vehicle’s steering ratio in function of the vehicle’s speed. As a result, better high speed vehicle’s control is achieved. The collision avoidance system works using infrared sensors around the vehicle, avoiding frontal and lateral collision. The distance to the obstacles is the parameter selected to avoid collisions (leaving the time for other actions like warnings to the driver). The fusion of the Autonomous Steer-by-Wire and the collisions avoidance system develops a semi-autonomous vehicle. This vehicle avoids collisions automatically, even if the driver does not avoid the collisions by himself, greatly reducing the probability of accidents.

scholarly journals An Investigation into the Appropriateness of Car-Following Models in Assessing Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7131
Author(s):  
Akito Higatani ◽  
Wafaa Saleh
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Full Range ◽  
Detection Range ◽  
Car Following ◽  
Car Following Model ◽  
System Module ◽  
Time Gap ◽  
Collision Avoidance System ◽  
Almost All

The dramatic progress of Intelligent Transportation Systems (ITS) has made autodriving technology extensively emphasised. Various models have been developed for the aim of modelling the behaviour of autonomous vehicles and their impacts on traffic, although there is still a lot to be researched about the technology. There are three main features that need to be represented in any car-following model to enable it to model autonomous vehicles: desired time gap, collision avoidance system and sensor detection range. Most available car-following models satisfy the first feature, most of the available car-following models do not satisfy the second feature and only few models satisfy the third feature. Therefore, conclusions from such models must be taken cautiously. Any of these models could be considered for updating to include a collision avoidance-system module, in order to be able to model autonomous vehicles. The Helly model is car-following model that has a simple structure and is sometimes used as the controller for Autonomous Vehicles (AV), but it does not have a collision avoidance concept. In this paper, the Helly model, which is a very commonly used classic car-following model is assessed and examined for possible update for the purpose of using it to model autonomous vehicles more efficiently. This involves assessing the parameters of the model and investigating the possible update of the model to include a collision avoidance-system module. There are two procedures that have been investigated in this paper to assess the Helly model to allow for a more realistic modelling of autonomous vehicles. The first technique is to investigate and assess the values of the parameters of the model. The second procedure is to modify the formula of that model to include a collision avoidance system. The results show that the performance of the modified full-range Auto Cruising Control (FACC) Helly model is superior to the other models in almost all situations and for almost all time-gap settings. Only the Alexandros E. Papacharalampous’s Model (A.E.P.) controller seems to perform slightly better than the (FACC) Helly model. Therefore, it is reasonable to suggest that the (FACC) Helly model be recommended as the most accurate model to use to represent autonomous vehicles in microsimulations, and that it should be further investigated.

Automating lane changes and collision avoidance on highways via distributed agreement

2019 ◽  
Vol 67 (12) ◽  
pp. 1047-1057
Author(s):  
Fabio Molinari ◽  
Aaron Grapentin ◽  
Alexandros Charalampidis ◽  
Jörg Raisch
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Hierarchical Control ◽  
Lane Changes ◽  
The Road ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Control Scheme ◽  
On The Road

Abstract This work presents a distributed hierarchical control strategy for fleets of autonomous vehicles cruising on a highway with diverse desired speeds. The goal is to design a control scheme that can be employed in scenarios where only vehicle-to-vehicle communication is available and where vehicles need to negotiate and agree on their positions on the road. To this end, after reaching an agreement on the lane speed with other traffic participants, each vehicle decides whether to keep cruising along the current lane or to move into another one. In the latter case, it negotiates the entry point with others by taking part in a distributed auction. An onboard controller computes an optimal trajectory transferring the vehicle with agreed velocity to the desired lane while avoiding collisions.

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