AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure

Car collision avoidance with velocity obstacle approach

The obstacle avoidance maneuver is required for an autonomous vehicle. It is essential to define the system's performance by evaluating the minimum reaction times of the vehicle and analyzing the probability of success of the avoiding operation. This paper presents a collision avoidance algorithm based on the velocity bstacle approach that guarantees collision-free maneuvers. The vehicle is controlled by an optimal feedback control named FLOP, designed to produce the best performance in terms of safety and minimum kinetic collision energy. Dimensionless accident evaluation parameters are proposed to compare different crash scenarios

Real-time optimisation-based path planning for visually impaired people in dynamic environments

Most existing outdoor assistive mobility solutions notify Visually Impaired People (VIP) about potential collisions but fail to provide Optimal Local Collision-Free Path Planning (OLCFPP) to enable the VIP to get out of the way effectively. In this paper, we propose MinD, the first VIP OLCFPP scheme that notifies the VIP of the shortest path required to avoid Critical Moving Objects (CMOs), like cars, motorcycles, etc. This simultaneously accounts for the VIP's mobility constraints, the different CMO types and movement patterns, and predicted collision times, conducting a safety prediction trajectory analysis of the optimal path for the VIP to move in. We implement a real-world prototype to conduct extensive outdoor experiments that record the aforementioned parameters, and this populates our simulations for evaluation against the state-of-the-art. Experimental results demonstrate that MinD outperforms the Artificial Potential Field (APF) approach in effectively planning a short collision-free route, requiring only 1.69m of movement on average, shorter than APF by 90.23%, with a 0% collision rate; adapting to the VIP's mobility limitations and provides a high safe time separation (>5.35s on average compared to APF). MinD also shows near real-time performance, with decisions taking only 0.04s processing time on a standard off-the-shelf laptop

Modeling collision avoidance maneuvers for micromobility vehicles

Introduction: In recent years, as novel micromobility vehicles (MMVs) have hit the market and rapidly gained popularity, new challenges in road safety have arisen, too. There is an urgent need for validated models that comprehensively describe the behaviour of such novel MMVs. This study aims to compare the longitudinal and lateral control of bicycles and e-scooters in a collision- avoidance scenario from a top-down perspective, and to propose appropriate quantitative models for parameterizing and predicting the trajectories of the avoidance—braking and steering— maneuvers. Method: We compared a large e-scooter and a light e-scooter with a bicycle (in assisted and non-assisted modes) in field trials to determine whether these new vehicles have different maneuverability constraints when avoiding a rear-end collision by braking and/or steering. Results: Braking performance in terms of deceleration and jerk varies among the different types of vehicles; specifically, e-scooters are not as effective at braking as bicycles, but the large e-scooter demonstrated better braking performance than the light one. No statistically significant difference was observed in the steering performance of the vehicles. Bicycles were perceived as more stable, maneuverable, and safe than e-scooters. The study also presents arctangent kinematic models for braking and steering, which demonstrate better accuracy and informativeness than linear models. Conclusions: This study demonstrates that the new micromobility solutions have some maneuverability characteristics which differ significantly from those of bicycles, and even within their own kind. Steering could be a more efficient collision- avoidance strategy for MMVs than braking under certain circumstances, such as in a rear-end collision. More complicated modelling for MMV kinematics can be beneficial but needs validation. Practical Applications: The proposed arctangent models could be used in new advanced driving assistance systems to prevent crashes between cars and MMV users. Micromobility safety could be improved by educating MMV riders to adapt their behavior accordingly. Further, knowledge about the differences in maneuverability between e-scooters and bicycles could inform infrastructure design, and traffic regulations

Comparative analysis & modelling for riders’ conflict avoidance behavior of E-bikes and bicycles at un-signalized intersections

With the increasing popularity of electric-assist bikes (E-bikes) in China, U.S. and Europe, the corresponding safety issues at intersections have attracted the attention of researchers. Understanding the microscopic behavior of E-bike riders during conflicts with other road users is fundamental for safety improvement and simulation modeling of E-bikes at intersections. This study compared the conflict avoidance behaviors of E-bike and conventional bicycle riders using field data extracted from video recordings of different intersections. The impact of conflicting road user type and gender on E-bikes and bicycles were analyzed. Compared with bicycles, E-bikes appeared to enable more flexibility in conflict avoidance behavior. For example, E-bikes would behave like bicycles when conflicting with motor vehicles/Ebikes, and behave more like motor vehicles when conflicting with bicycles/pedestrians. Based on this, we built an Extended Cyclist Conflict Avoidance Movement (ECCAM) model, which can represent the conflict avoidance behavior of E-bikes/bicycles at mixed traffic flow un-signalized intersections. Field data were applied to validate the proposed model, and the results are promising

Safety Applications and Measurement Tools for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Drive Video Analysis for the Detection of Traffic Near-Miss Incidents

Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201