Abstract:During the road accident analysis speed of the examined vehicle often is being established in accordance with the length of the trace remained by the vehicle tires. Usually in such case there are values of braking deceleration used for calculations, which are selected from the corresponding references. The real experiment, establishing the braking parameters of the given vehicle, is performed more seldom. In both cases it is important to calculate the vehicle speed in accordance with the received or found valu… Show more
“…The maximum braking distance at any speed is upper bounded by a linear function of speed: where Cstop∈R≥0. To see why Dbrake,max can be upper-bounded by a linear function of speed, consider the following example. For automobiles, the maximum braking distance is proportional to the kinetic energy of the vehicle, which is proportional to the square of the vehicle’s speed (Nagurnas et al, 2007). Figure 16 shows that this relationship holds for the high-fidelity models of the Segway and Rover robots described in Section 8. Fig.…”
Section: B1 Proof Of Theorem 39mentioning
confidence: 99%
“…To see why Dbrake,max can be upper-bounded by a linear function of speed, consider the following example. For automobiles, the maximum braking distance is proportional to the kinetic energy of the vehicle, which is proportional to the square of the vehicle’s speed (Nagurnas et al, 2007).…”
To operate with limited sensor horizons in unpredictable environments, autonomous robots use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while creating the next plan. However, creating safe, dynamically feasible trajectories in real time is challenging, and planners must ensure persistent feasibility, meaning a new trajectory is always available before the previous one has finished executing. Existing approaches make a tradeoff between model complexity and planning speed, which can require sacrificing guarantees of safety and dynamic feasibility. This work presents the Reachability-based Trajectory Design (RTD) method for trajectory planning. RTD begins with an offline forward reachable set (FRS) computation of a robot’s motion when tracking parameterized trajectories; the FRS provably bounds tracking error. At runtime, the FRS is used to map obstacles to parameterized trajectories, allowing RTD to select a safe trajectory at every planning iteration. RTD prescribes an obstacle representation to ensure that obstacle constraints can be created and evaluated in real time while maintaining safety. Persistent feasibility is achieved by prescribing a minimum sensor horizon and a minimum duration for the planned trajectories. A system decomposition approach is used to improve the tractability of computing the FRS, allowing RTD to create more complex plans at runtime. RTD is compared in simulation with rapidly-exploring random trees and nonlinear model-predictive control. RTD is also demonstrated in randomly crafted environments on two hardware platforms: a differential-drive Segway and a car-like Rover. The proposed method is safe and persistently feasible across thousands of simulations and dozens of real-world hardware demos.
“…The maximum braking distance at any speed is upper bounded by a linear function of speed: where Cstop∈R≥0. To see why Dbrake,max can be upper-bounded by a linear function of speed, consider the following example. For automobiles, the maximum braking distance is proportional to the kinetic energy of the vehicle, which is proportional to the square of the vehicle’s speed (Nagurnas et al, 2007). Figure 16 shows that this relationship holds for the high-fidelity models of the Segway and Rover robots described in Section 8. Fig.…”
Section: B1 Proof Of Theorem 39mentioning
confidence: 99%
“…To see why Dbrake,max can be upper-bounded by a linear function of speed, consider the following example. For automobiles, the maximum braking distance is proportional to the kinetic energy of the vehicle, which is proportional to the square of the vehicle’s speed (Nagurnas et al, 2007).…”
To operate with limited sensor horizons in unpredictable environments, autonomous robots use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while creating the next plan. However, creating safe, dynamically feasible trajectories in real time is challenging, and planners must ensure persistent feasibility, meaning a new trajectory is always available before the previous one has finished executing. Existing approaches make a tradeoff between model complexity and planning speed, which can require sacrificing guarantees of safety and dynamic feasibility. This work presents the Reachability-based Trajectory Design (RTD) method for trajectory planning. RTD begins with an offline forward reachable set (FRS) computation of a robot’s motion when tracking parameterized trajectories; the FRS provably bounds tracking error. At runtime, the FRS is used to map obstacles to parameterized trajectories, allowing RTD to select a safe trajectory at every planning iteration. RTD prescribes an obstacle representation to ensure that obstacle constraints can be created and evaluated in real time while maintaining safety. Persistent feasibility is achieved by prescribing a minimum sensor horizon and a minimum duration for the planned trajectories. A system decomposition approach is used to improve the tractability of computing the FRS, allowing RTD to create more complex plans at runtime. RTD is compared in simulation with rapidly-exploring random trees and nonlinear model-predictive control. RTD is also demonstrated in randomly crafted environments on two hardware platforms: a differential-drive Segway and a car-like Rover. The proposed method is safe and persistently feasible across thousands of simulations and dozens of real-world hardware demos.
“…To see why D brake,max can be upper-bounded by a linear function of speed, consider the following example. For automobiles, the maximum braking distance is proportional to the kinetic energy of the vehicle, which is proportional to the square of the vehicle's speed [33]. Let D quad : [0, v max ] → R ≥0 be the braking distance of this quadratic relationship.…”
Autonomous mobile robots must operate with limited sensor horizons in unpredictable environments. To do so, they use a receding-horizon strategy to plan trajectories, by executing a short plan while creating the next plan. However, creating safe, dynamically-feasible trajectories in real time is challenging; and, planners must ensure that they are persistently feasible, meaning that a new trajectory is always available before the previous one has finished executing. Existing approaches make a tradeoff between model complexity and planning speed, which can require sacrificing guarantees of safety and dynamic feasibility. This work presents the Reachability-based Trajectory Design (RTD) method for trajectory planning. RTD begins with an offline Forward Reachable Set (FRS) computation of a robot's motion while it tracks parameterized trajectories; the FRS also provably bounds tracking error. At runtime, the FRS is used to map obstacles to the space of parameterized trajectories, which allows RTD to select a safe trajectory at every planning iteration. RTD prescribes a method of representing obstacles to ensure that these constraints can be created and evaluated in real time while maintaining provable safety. Persistent feasibility is achieved by prescribing a minimum duration of planned trajectories, and a minimum sensor horizon. A system decomposition approach is used to increase the dimension of the parameterized trajectories in the FRS, allowing for RTD to create more complex plans at runtime. RTD is compared in simulation with Rapidly-exploring Random Trees (RRT) and Nonlinear Model-Predictive Control (NMPC). RTD is also demonstrated on two hardware platforms in randomly-crafted environments: a differential-drive Segway, and a car-like Rover. The proposed method is shown as safe and persistently feasible across thousands of simulations and dozens of hardware demos.
“…Authors decided to use IR spectroscopy to solve this problem. IR spectroscopy is a technology used to identify substances [4][5][6] and, according to author's opinion, it will be possible to use it to identify the braking trace at the scene of a road accident [7][8][9][10]. Preliminary studies have been performed.…”
Skid marks are an important source of information for road accident investigators. Up till now, analyzing them was a difficult task when ABS equipped vehicles are considered. Authors managed to solve this issue using IR spectroscopy. There are several factors to consider when investigating brake marks and one of them is slip ratio that was taken into account in this paper. Results show that for slip of 10% and more, IR spectroscopy technology was successful at revealing skid marks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
