On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Kim, Min-Joong; Yu, Sung-Hun; Kim, Tong-Hyun; Kim, Joo-Uk; Kim, Young‐Min

doi:10.3390/s21206733

Cited by 6 publications

(3 citation statements)

References 33 publications

(35 reference statements)

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“…If the adjacent lanes' safe distances are satisfied, then the EV is able to perform the lanechanging behavior, and if that is not the case, the safe distance will also take effect in the car-following scenarios. The detailed description and the graphic representation of the RSS safe distance can be found in [43].…”

Section: Decision-making Proceduresmentioning

confidence: 99%

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Wang

Song

et al. 2023

Sustainability

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Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this paper to address the problem mentioned above in environments with multiple lanes and surrounding vehicles. Firstly, high-level decision making is implemented by a finite-state machine (FSM), according to the relative relationship between the ego vehicle (EV) and the surrounding vehicles. After the decision is made, a cluster of quintic polynomial equations is established to generate the path connecting the initial position to the candidate target positions, according to the traffic states of the EV and the target vehicle. The optimal path is selected from the cluster, based on the quadratic programming (QP) framework. Then, the speed profile is generated, based on the longitudinal displacement–time graph (S–T graph), considering the vehicle motion state constraints and collision avoidance. The smoothed speed profile is created through another QP formulation, in the space created by the dynamic-programming (DP) method. Finally, the planned path and speed profile are combined and sent to the lower level control module, which consists of the linear quadratic regulator (LQR) for lateral trajectory tracking control, and a double PID controller for longitudinal control. The performance of the proposed framework was validated in a co-simulation environment using PreScan, MATLAB/Simulink and CarSim, and the results demonstrate that the proposed framework is capable of addressing most ordinary scenarios on a structured road, with reasonable decisions and controlling abilities.

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Section: Decision-making Proceduresmentioning

confidence: 99%

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Wang

Song

et al. 2023

Sustainability

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“…To detect safety violations, if any, during the simulation of a complete solution (i.e., the combination of a scenario and an MLC behavior), we measure the distance between the ego vehicle and the vehicle in front for each simulation time step. Based on feedback from domain experts and values provided in [42,46], the value of 1.5 m was chosen for 𝑑 𝑚𝑖𝑛 for the evaluation, which is reasonable as minimum distance behind a stopped car in the front since AVs that have much quicker reaction times than humans [47,48]. If the distance is less then 𝑑 𝑚𝑖𝑛 at any time, the violation is detected and the complete solution is marked as unsafe.…”

Section: Evaluation Subjectsmentioning

confidence: 99%

Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search

Sharifi¹,

Shin²,

Briand³

et al. 2023

Preprint

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In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such boundary captures the conditions in terms of MLC behavior and system context that can lead to hazards, it can then be used to, for example, build a safety monitor that can take any predefined fallback mechanisms at runtime when reaching the hazard boundary. However, determining such hazard boundary for an ML component is challenging. This is due to the space combining system contexts (i.e., scenarios) and MLC behaviors (i.e., inputs and outputs) being far too large for exhaustive exploration and even to handle using conventional metaheuristics, such as genetic algorithms. Additionally, the high computational cost of simulations required to determine any MLAS safety violations makes the problem even more challenging. Furthermore, it is unrealistic to consider a region in the problem space deterministically safe or unsafe due to the uncontrollable parameters in simulations and the non-linear behaviors of ML models (e.g., deep neural networks) in the MLAS under analysis. To address the challenges, we propose MLCSHE (ML Component Safety Hazard Envelope), a novel method based on a Cooperative Co-Evolutionary Algorithm (CCEA), which aims to tackle a high-dimensional problem by decomposing it into two lower-dimensional search subproblems. Moreover, we take a probabilistic view of safe and unsafe regions and define a novel fitness function to measure the distance from the probabilistic hazard boundary and thus drive the search effectively. We evaluate the effectiveness and efficiency of MLCSHE on a complex Autonomous Vehicle (AV) case study. Our evaluation results show that MLCSHE is significantly more effective and efficient compared to a standard genetic algorithm and random search.

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“…Benchmarking the findings of Khoudour et al ( 22 ), which attest to violation of stop signs by 14% of drivers and 29% of cases related to level crossing operation errors, it is of paramount significance to espouse other strategies for reducing collisions at level crossings. A wealth of research supports the potential for autonomous vehicles (AVs) to maintain critical safety while driving ( 23 ).…”

mentioning

confidence: 99%

Optimization-Based Traffic Safety Improvement Strategy for Autonomous Vehicle Driving at Level Crossings

Adane

Hou

Peng

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Railway level crossings pose a serious threat to the safety and mobility of drivers traversing them. Globally, a significant number of traffic violations occur annually at level crossings. This study proposes an optimization-based autonomous vehicle (AV) driving strategy to improve the traffic safety of AVs at level crossings. First, an optimization model considering the operational constraints is developed. The cost function of the proposed optimization-based driving strategy is the time difference between the time required by the train to reach the level crossing and the time taken by the AV to complete the whole crossing maneuver. Second, a MATLAB simulation model is developed to validate the proposed driving strategy. Simulation results show that the proposed driving strategy delivers optimized AV driving without stoppage. The comparative study also shows that the proposed method improves driving performance. For instance, when a train is 1 m away from the light signal while the AV’s initial speed is 36 km/h, the time required for the AV to reach the speed limit sign is about 0.44 s, while the time required by the train to reach the car block area is 0.13 s. Thus, the AV is forced to stop and then go after the train leaves the level crossing. Contrary to this, the proposed method allows a safe passage of the vehicle within 1.66 s without stoppage. Most importantly, the driving strategy is a collision avoidance strategy without unnecessary enforcement of stop-and-go when AVs have the chance of crossing safely. Consequently, the optimization-based driving strategy both improves traffic safety and reduces traveling time.

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On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Cited by 6 publications

References 33 publications

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search

Optimization-Based Traffic Safety Improvement Strategy for Autonomous Vehicle Driving at Level Crossings

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