Motion Modelling in Self-Driven Cars

Motion Modelling in Self-Driven Cars


Driverless vehicle technology started long ago in the 1920s. One of the significant milestones in this technology was the DARPA Grand Challenge which was in 2004. The objective of this challenge was for these cars to navigate a 150 mile off the road as fast as possible. This was among the significant challenges considering that there was no human involvement during the racing of the cars. Eliminating human intervention during the race was close to impossible due to the technicalities that required the presence of human intervention. None of the vehicles that entered the event completed the race. A similar event was held in 2005 this time about 5 of the 23 teams getting to the finish line. Later in 2007, a DARPA Urban Challenge was held where the vehicles were expected to drive autonomously within a simulated urban set-up. Six of the teams finished the race which therefore showed that autonomous urban driving is an issue that was possible. Tests and numerous events have since the challenges organized by DARPA. Engineers and researchers have been on the verge of discussion as well as researching some of the safest means that can be used with the self-driving cars in the urban set-up. Before these vehicles are released, it is essential that appropriate research is conducted so as to establish their effectiveness within the urban setting. It will also be critical to evaluate some of the salient features of these vehicles to come up with a mechanical model that is crucial in controlling as well as modeling of these vehicles. Various algorithms will be critical in the modeling process with the use of key mathematical analysis which will help in the set-up of these vehicles.






  • SLAM Simultaneously Localization And Mapping. Algorithm for mapping the surroundings of an agent and localizing that agent within the map.
  • EKF Extended Kalman Filter. Algorithm for approximating the behavior of a function.
  • CVCTR Constant Velocity Constant Turn Rate. A motion model that will be discussed in the paper



















The Motion Model of Autonomous Vehicles

Motion model is a mathematical model that models an object’s motion as a function of time. In the autonomous vehicle, the motion model is a critical part of SLAM because SLAM is the solution for two specific problems: what does the world look like and where am I. In order to keep tracking positions in a SLAM algorithm, motion models are widely used to predict object’s place after a certain amount of time, then other inputs from other sensors will give another prediction, then we use some filters like EKF to filter the noises and provide a more accurate prediction. The motion model can be very simple, assume the object is only moving in one direction at a constant speed, then the motion model can be just the product of elapsed time and velocity. The motion model can be extremely complicated, for example, we can use a simple linear model described above when the car is at a straight line, or it’s activating cruising control, and use another nonlinear model when the car is at a curve, which considers the angular velocity, and maybe yet another model to describe the dynamics the car made when it’s sliding. Because of the variety of real-world situation, the vehicle can be in, a motion model can be super complicated depend on the assumption you made and the situation you faced. In most tracking systems, the target motion is modeled as a system whose varying state makes a transition according to an underlying model or several switching models.


Constant Velocity Constant Turn Rate Model

The most simple motion model can be the white-noise acceleration model (1), which assume that the acceleration is an independent process. In addition, there’s constant acceleration model (1), which implies the acceleration is a process with independent increments, Singer acceleration model (2), which assumes the acceleration to 2 be a time-correlated stochastic process. Engineers have introduced much more models over the years, they all perform great under a specific situation, but there are no optimal models for general uses, CVCTR is one of such models that may fit into general applications.

Segmenting Track Identifier (STI)

Within the constant velocity constant turn rate model, there exists a segmenting track identifier (STI). STI is the non-Bayesian tracker proposed as an alternative to the maneuvering tracking. The data-driven estimator in most cases partitions a given target trajectory into segments as well as estimating the state parameters in each of the segments. It operates effectively in the tracking of a target that is highly maneuvering and has behavior that is unknown. The system does not function well in systems where the target motion is smooth and possible to predict. One of the critical components of STI is the approximation of one segment as the circular arc or straight line that can be treated as the arc that has a definite radius.

Motion modeling is a significant field in robotics related issues. A motion model is a model that gives the robot kinetic information as well as provides an output of the next position of the robot. Kinematic information of robots could be different, but usually, it includes acceleration and the wheel RPM.

Motion model together with the sensory input so as to predict the location of the robot. In the self-driving formula style vehicle, there is a need for coordination of the critical elements of the car such that communication can be made possible and regulate the traffic movement of these cars. The paper addresses motion planning as well as control of the autonomous and heavy-duty industrial vehicles for instance buses and trucks by the use of optimization-based techniques. Among the rapidly expanding technologies is the autonomous driving which promises to play a significant role in the future because of its energy efficiency, convenience as well as the safe transport system that is exhibited by the motion cars. The autonomous cars are anticipated to have their initial impact in environments that are closed such as in mining and construction areas. Self-driving vehicles is a maturing technology that has the potential of reshaping mobility through enhancing of the aspects of efficiency, safety, accessibility as well as convenience in automotive transport.

The self-driven cars need to be safe, and as such, some of the tasks executed here include planning for the motions through the dynamic environment which is shared by pedestrians, other vehicles, and other road users. The robust execution of these self-driven cars needs to be governed by a feedback control system which will ensure that there is effective movement in the roads. The major objective of this research is to analyze the present state of planning as well as control algorithms particularly in relation to an urban setting. The selection of the techniques that have been proposed is reviewed together with the effectiveness of these techniques as well as the computational prerequisites.

Motion Planning

Motion planning for autonomous vehicle corresponds to solving standard motion planning issue just as discussed within the robotics literature. Many at times, the exact solutions to the motion planning issue are computationally intractable. The use of numerical approximation methods will be used in the actual practice so as to come up with practical analysis of the problem. Among the popular mathematical approaches include variation method which poses a problem as the non-linear optimization in function space as well as graph search approaches which will construct graphical discretization of the state space of the vehicle. This will additionally search for the shortest path through the use of graph search method as well as the incremental tree approaches. These approaches incrementally construct the tree of reachable states from an initial vehicle state and eventually select the effective branch in the tree.

Model for Planning and Control

In the modeling for planning and control, the model commonly user are those for mobility of car-like vehicles. These models are used widely in the control and motion planning algorithms in the approximation of the vehicle’s behavior in response to the controls that are in the relevant operating aspects and conditions. This section will look into the general modeling guidelines as well as some of the techniques used in motion planning and control. The analysis of the pathway will be determined using the mathematical models that will help provide an appropriate analysis into the self-driven cars. Modeling starts with vehicle configuration which is the representation of the pose or the position of the vehicle in the world. With the coordination of all the necessary elements, it is thus possible to determine the motion of the vehicle as well as the trajectory of the vehicle in motion.

Intelligent Autonomous Robotic Automobile (IARA)

The Intelligent Autonomous Robotic Automobile (IARA) is an autonomous automobile which uses path planner in the computation of the path from the current position to the destination that is desired by the users. Using the path planning process and the current position, the mapping process could be used in controlling the movement and computation of smooth trajectories from the current position to the destination in about 50ms. The autonomous car consists of the hardware platform as well as the collection of software modules tasked with localization, mapping, path planning, motion planning, and traffic lights detection, high-level, sophisticated decision making as well as simultaneous localization and mapping (SLAM). In the instance of IARA, the Ford Escape Hybrid system has been used as the platform for designing as well as synchronization of a set of hardware components which will allow the vehicle to operate within the urban traffic set-ups.

Within a typical mission, the IARA is at the initial pose 𝐩𝑜 = (𝑥𝑜, 𝑦𝑜, 𝜃𝑜), and the user will define the end-pose, 𝐩𝑒 = (𝑥𝑒, 𝑦𝑒, 𝜃𝑒), and asks it to go from 𝐩𝑜 to 𝐩𝑒. When asked to perform a mission, the IARA path planner will build a pathway 𝑃 = (𝐩𝐯𝑜… 𝐩𝐯𝑖,…, 𝐩𝐯𝑒), from 𝐩𝑜 to 𝐩𝑒, that is composed of the vector of poses at about 0.5m apart as well as the associated velocities 𝐩𝐯𝑖 = (𝑥𝑖 , 𝑦𝑖 , 𝜃𝑖 , 𝑣𝑖), which goes from 𝐩𝐯𝑜 = (𝑥𝑜, 𝑦𝑜, 𝜃𝑜, 𝑣𝑜) to 𝐩𝐯𝑒 = (𝑥𝑒 , 𝑦𝑒 , 𝜃𝑒 , 𝑣𝑒), while at the same time obeying the instructions and protocols that have been imposed by the road limits. The operational parameters, in this case, will be elements such as maximum speed, acceleration as well as the rate of the driving wheel turn. With a robotic analysis as well as appropriate programming mechanisms, it will be potentially possible to control the self-driven vehicles as well as regulate the speed at which they are traveling. The only challenge in such a case will be the analysis of the traffic so as to determine regions where the traffic is relatively manageable. However, with the combination of the traffic lighting system and the primary analysis of the major protocols that define the movement and speed of these self-driven vehicles, it will be possible to effectively control the traffic.

In a predictive model, the trajectory generation algorithm is used in the computation of the trajectories which satisfy a desired goal state constraints. The paths are often computed using a set of optimization procedures that run on various control parameters. Constraint equation is defined as the difference that exists between goal state constraint and the integral car model dynamics. These are some of the critical elements that will help determine the trajectory as well as the speed with which a self-driven vehicle will move. Constrain trajectory algorithm will determine the control parameters which will thus minimize the constraint equation in the process. Approximate mapping from the state space to parameter space will be pre-computed offline so as to set the pace for constraint optimization process. The evaluation of the resulting trajectories is then done through the use of different evaluation criteria.



White Noise Acceleration Model

White Noise Acceleration Model is the simplest model for the target maneuver. It assumes target acceleration  (t) as an independent process. White Noise Acceleration Model differs from the non-maneuver model of Subsection in the level of noise. The white noise model is used in modeling the effect that can be brought about by the various control inputs. A maneuver in itself is aimed at accomplishing a given task and as such an independent in relation to time. The only thing that is attractive with this model is the aspect of simplicity. It is normally used when the maneuver is random and small. It can only be used in the adjustment of the noise level.

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