Highway Safety and Traffic Flow Analysis of Mixed traffic with Connected and Non-Connected Vehicles

Safety is the number one issue in the deployment of any vehicle technology. This leads to two interconnected challenges. First, how to ensure safety without having a significant negative impact in traffic flow. Second, how will varying penetrations of autonomous vehicles (AVs) impact safety and efficiency in mixed traffic. To address these issues, we start by proposing a risk metric that takes into account the severity of a collision that would happen under a worst-case scenario and the time the vehicle is exposed to such a collision. With this definition, we propose an autonomous lane changing procedure in which the vehicle behaves as if it was simultaneously on both lanes. This ensure that the vehicle never puts itself in a collision prone situation. Given the conservative nature of this approach, which can negatively impact traffic flow, we include the possibility of the AV accepting risks in its gap acceptance decision process. We extend this approach to a scenario with connected and autonomous vehicles (CAV), which can cooperate to generate lane change gaps through communications. In this case, a CAV in the destination lane also behaves as if it was simultaneously on two lanes, thus generating the gap for the incoming vehicle. We perform extensive micro simulations using the commercial software VISSIM with varying percentages of AVs and CAVs, different vehicle inputs, and several accepted risk values. Results indicate that, while AVs need to accept small risks in order to achieve the same traffic flow efficiency as humans, CAVs can improve both safety and efficiency without having to accept any risks. Our results also indicate that AVs and CAVs still behave safely in mixed fleets, but they do not bring significant improvements in traffic flow

Assistente avançado de suporte ao motorista para redução de risco de tombamento de veículos pesados em curva.

No Brasil, o transporte rodoviário é responsável por 58% do transporte de carga, que tem os acidentes como um grande problema, pois, em geral, esses ocasionam muitas vítimas, prejuízos econômicos relevantes e em alguns casos danos ambientais decorrentes de derramamento de carga. Estudos apontam que os prejuízos com os acidentes no transporte de carga em 2012 foram de mais de 9 bilhões de reais. Estudo realizado em 2007 pela PAMCARY, corretora de seguros e gestora de riscos, revelou que os eventos que combinam maior frequência e gravidade são tombamento e capotagem. Nesse sentido, esta pesquisa consiste na elaboração de um assistente avançado para motorista que objetiva alertar previamente sobre a velocidade limite da curva, a fim de diminuir os riscos de tombamento. Em outras palavras, consiste em buscar mitigar o problema auxiliando o motorista para que ele mantenha o veículo em uma velocidade segura, por meio de alertas e em prazo adequado, que permitam ao motorista tomar medidas corretivas em caso de estado inseguro. A solução foi desenvolvida a partir de uma arquitetura modular, que funciona da seguinte forma: por meio de sensores (velocidade, GPS e posição do acelerador), associado a mapas digitais, o risco de acidente é controlado constantemente. Com isso, um dispositivo poderia ser embarcado na cabine do veículo para emitir alertas visual e auditivo de risco de tombamento. A solução utiliza o indicador de estabilidade chamado Limiar Estático de Tombamento que, associado à informação a priori de mapas digitais, permite o cálculo do risco de tombamento com diferentes abordagens. No contexto da pesquisa, foram desenvolvidas 04 versões de assistentes. Além disso, foi proposto um arcabouço de simulação microscópica de trânsito baseado no modelo de raciocínio prático denominado de belief-desire-intention (BDI) para permitir o desenvolvimento e a validação de agentes inteligentes para Sistemas Avançados de Assistência ao Motorista de maneira rápida, flexível e fácil. Para avaliar o potencial dos assistentes, foi escolhida a BR-101, estrada federal de Alagoas com mais ocorrências de tombamento. Nessa rodovia, foram simulados 400 veículos para avaliar o desempenho dos assistentes propostos. Em particular, foram investigadas a efetividade, intrusividade, omissão e a segurança para avaliar o desempenho dos assistentes.In Brazil, highway transportation is responsible for 58% of cargo transport. A relevant problem associated to cargo transport are the accidents, that generally cause an elevated number of victims, relevant economic losses and, in some cases, damages to the environment due to cargo spills, since there are also dangerous products being transported. Researches point out that the cost of accidents in cargo transportation in 2012 was more than BRL 9 billion. A study performed in 2007 by PAMCARY revealed the accidents profile: the events that combine higher frequency and gravity are rollover and tipping (considered here as the same nature). In this study, incompatible speed and fatigue, factors that are related to human actions, were pointed out as main causes of accidents; for another hand, sharp curve and poorly maintained roads are contributing factors to accidents. Therefore, the research proposal consists of the adoption of an assistant for warning in advance of over speed for a specific curve. This may reduce rollover risks. In other words, it would be mitigated the problem by helping the driver to maintain the vehicle in a safe speed, through customized alerts just in time to allow the driver to take corrective maneuvers in case of unsafe state. The solution is a modular architecture, which works as follows: through sensors (speed, GPS and throttle position) associated with digital maps, it is controlled the risk of accident constantly. With that, an embedded device at the vehicle’s cab could to emit visual and sound alerts warning the risk of rollover. In this work, it is proposed the adoption of the stability indicator known as Static Rollover Threshold, which is combined with a priori information from digital maps to allow the calculation of the rollover risk by different approaches. In the context of this research, 04 versions of assistants were developed. In addition, a microscopic traffic simulation framework was proposed based on the practical reasoning model named belief-desire-intention (BDI) to support the development and validation of intelligent agents for Advanced Driver Assistance Systems in a fast, flexible and easy way. To evaluate the assistants’ potential, the BR-101, Federal Highway of Alagoas with more occurrence of rollover, was chosen. On this highway, 400 vehicles were simulated to evaluate the performance of the proposed assistants. The effectiveness, intrusiveness, omission and safety of the assistants were investigated